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The synthesiser uses a speech corpus originally designed for the Polish BOSS unit selection synthesiser. The BOSS architecture follows the client-server paradigm. BOSS performs the synthesis using a two-stage algorithm: first, the candidate selection is performed, where units of the highest possible level word, syllable or phoneme are selected from the corpus.

For the Polish BOSS, the speech material 4 hours was read by a professional radio speaker during several recording sessions, and later annotated on word, syllable and phone levels by trained phoneticians, with suprasegmental information about prosody of utterances [4]. It uses a non-uniform unit selection technology. In this approach, a vector of ca. On this basis, F 0 and duration contours are generated. Next, polyphones are selected from a large speech database according to the model and concatenation cost functions.

After applying limited time-scale modifications to the pitch, duration and power of selected units, the units are concatenated using the Pitch Synchronous Overlap and Add PSOLA algorithm.

The system is dedicated for the judicature, the police, the border guards and other public security services. The system is designed to assist the user in writing notes, protocols, formal texts, legal documents, police reports as well as other tasks connected with the domain.

The acoustic models for the ASR system have been trained on the Jurisdict database which contains speech material of about speakers from all parts of Poland. These sentences were created for research purposes to provide triphone and diphone coverage and to cover the most important syntactic structures.

In the present study, results on 7 levels are discussed. Additionally, the ASR system provides a speaker adaptation procedure which renders the system speaker-dependent and improves the results of speech recognition. The system accuracy tends to saturate at ca. Speech material 4. Speech recognition — police report An anonymised police report1 was chosen as a text to be synthesised.

This choice was justified by the particular domain of the ASR system. The report has a typical structure and consists of words. Beginning with general information about the suspect, and followed by his confession of guilt and explanations, it ends with a final statement of submitting to the punishment. The report was read and recorded by an expert user of the ASR system, Piotr. In order to provide better control and easier data analysis at all stages of the experiment, the report was divided into 44 small pieces at the end of the sentences or at pauses in the speech signal.

The original human speech was automatically annotated on the phone level using SALIAN [15] and checked manually by a phonetician. Because the original recordings were of male voice with male F 0 values and PL1 1 All the names in the police report are fictional and should not be linked to real people.

The set of sentences assures the most common triphone coverage in the Polish language and typical phrases and vocabulary for the domain of the ASR, i. The set was designed especially for the Pozna ASR system. Level 1 shows the lowest accuracy of recognition, but works fastest. On the other hand, Level 7 shows the best recognition results, but the recognition time is longer. The workflow of the experiment is presented in Figure 2. PL2 voice diphone database is based on the natural voice of Mariusz which is included in the Jurisdict database.

In the experiment the recording of the natural voice of Piotr was used — Piotr was also recorded earlier and is one of the speakers who gave their voices for the Jurisdict database. Natural voices are drawn in gray cylinders. The generated synthetic speech of the police report was evaluated by the ASR system. The generated synthetic sentences used for the speaker adaptation was also input to the ASR system and the system was trained to improve speech recognition.

The evaluation part in Figure 2 is marked in hatching and includes recognition of the original human speech. The ASR outputs text data whose analysis is discussed below. Synthetic speech evaluation with speaker adaptive automatic speech … 49 Figure 1.

The workflow of the experiment 6. Results 6. The results show that on almost all the levels the human speech was recognised best. The diphone concatenative and statistical parametric synthesisers produced speech which had the highest recognition accuracy without adaptation, and the statistical parametric synthesis was best recognised after speaker adaptation, exceeding even the results of recognition of human speech. The worst scores with and without speaker adaptation were obtained by the unit-selection speech synthesis, although it is worth underlining that the speaker adaptation increased the accuracy of speech recognition by 26pp, and reduced the real time factor by Markov models.

Additionally, the HTS generates very natural prosody and renders virtually no mismatches at the segment boundaries. After speaker adaptation, the mistakes in word and sentence recognition were similar. All these words were substituted by other words.

These words are present in the ASR dictionary and the system was be able to recognise them, but it did not. Conclusions and discussion In this paper, the evaluation of speech synthesis systems was performed using the speaker adaptive automatic speech recognition system for Polish. The ACCS synthesis with Mbrola based on concatenation of diphone also scored very high when the ASR acoustic models were not trained on the adaptation set sentences.

The unit-selection systems obtained the worst results of accuracy recognition and also the time of recognition was the slowest. Human speech was recognised very well, with and without speaker adaptation, although the HTS synthesis exceeded its accuracy results at level 7 after speaker adaptation by 1. The high accuracy recognition of the synthetic speech for the modern HTS speech synthesiser shows that this technology can successfully be used in communicative situations when human speech is replaced by synthetic voices and when the human ear is replaced by the automatic speech recognition system.

Additionally, the results show that the automatic speech recognition may be used by people with different speech impairments if the acoustic models are adapted. References [1] Mehler, A. Handbook of Technical Communication. Automatic Close Copy Speech Synthesis. In: Speech and Language Technology. Polish speech dictation system as an application of voice interfaces. Communications in Computer and Information Science, Vol.

Philadelphia, pp. Communicative Alignment of Synthetic Speech. Institute of Linguistics. Adam Mickiewicz University in Pozna. August Polish unit selection speech synthesis with BOSS: extensions and speech corpora. Transcription-based automatic segmentation of speech.

Relatively good results were obtained already in , however, the contribution of the applied language model was still unsatisfactory, in the case of a strongly inflectional Polish language. Also the decoding time was too high.

The paper reports briefly process in which the most effective results in precise speech recognition were accomplished through systematic evaluation and validation of the system. System effectiveness was tested on big sets and information was gained from future users. Introduction In the years the prototype of the Automatic Speech Recognition System ASR has been prepared within two development projects financed by the Ministry of Science and Higher Education and then adjusted to implementation [3, 4, 5].

The system is designed for law firms and legal services agencies, judiciary and police force. The system relies on two corpora: JURISDICT corpus created in order to provide statistical acoustic models optimum for speech recognition and used for testing the models and the language corpora consisting of the linguistic material collected for the project purpose.

Laboratory tests Since speech quality is the key factor to speech recognition correctness a text should be dictated audibly, with moderate volume and paste not too fast.

The quality of dictated speech influences also speech recognition time. The better the speaker, the higher the percent of correctly recognized words and the shorter the decoding time. Every mispronunciations and falters, voice indisposition like runny nose or hoarseness, as well as cough, noisy breath or smacking noises affect negatively the quality of speech recognition.

To all speech shaped sounds categorized as a word ASR assigns the most probable word from the system dictionary. Therefore a speaker is requested to pronounce the text with high precision and accuracy.

The system works then fast and smoothly. When microphones with characteristics different than those applied for learning the system non-dedicated microphones were used, the correctness of speech recognition worsened. After adaptation process the results achieved using dedicated and non-dedicated microphones become similar. Adaptation to speaker voice, the microphone used, the other electro-acoustic device and acoustical environments improves the speech recognition results and shorten the recognition time [3, 5].

Correctness verification method Evaluation tests have been carried out with using the standard Sclite tool for speech recognition system assessment. The application compares the reference text and recognized text and calculates recognition correctness as an average percent of correctly recognized words including in minus interpolated words. The evaluation of recognition correctness is made based on calculating the Levenshtein distance which is defined as the least number of simple operations changing one sequence of words into the other sequence.

Simple operations mean to insert new word, delete a word or replace a word into another one. The distance between two sentences is the least number of simple operations transforming one sentence into another one. In this way recognition errors inserted by decoder can be separated from language model influence on results obtained in tests.

Without the above generalization the described tool is compatible to Sclite. Acoustic model testing During the tests of the ASR system many variants of acoustic models were taken into account.

Tests with several dozen variants of acoustic models were carried out, including among others LDA models, MFCC with different numbers of Gaussian mixtures, physical states and subsequent learning iterations within the range from 1 to 6.

Tables 1 and 2 below and the Figure 3 show partial results for given tests. Table 3. The effect of the senon number of acoustic model on its size and speech recognition time and accuracy. Tests made on a sub-set containing of randomly chosen statements, words of the validation set I 97 speakers using universal acoustic model without adaptation.

The effect of the number of acoustic model iterations on recognition time and accuracy. The effect of the number of acoustic model Gaussian mixtures on speech recognition time and accuracy for 7 recognition quality levels. Tests made on a sub-set containing randomly chosen statements, words of validation set I 97 speakers using universal acoustic model.

The test results showed that the most efficient and accurate recognition is achieved with the acoustic model trained using recordings sub-set from the JURISDICT corpus about 1,5 mio recordings, approx.

Language model tests Due to the big size of the acoustic model over 1,3 GB tests concerning possible size reduction have been carried out. Language models based on juridical corpus and press material texts for different cuts number of presence in learning corpus , bigrams within the range from 1 to 9 and trigrams within the range from 1 to 30 were tested.

The models were built based on joined corpus of juridical and press texts and using interpolation models built on separated corpora, interpolation means using the given model with chosen weight. Additionally, input reduction of bigrams and trigram lists has been made due to their information rate further reduction of acoustic model size. In total variants of acoustic models have been tested.

The size of the previously used model was MB, whereas the size of the currently applied model is MB. Optimization of speech recognition speed Within three and a half years of work of the Laboratory of Integrated Speech and Language Processing Systems team several dozen versions of the ARM system have been developed. The optimization included the improvement of the algorithm for cutting result word matrix. Thereby the optimization influence on the accuracy and the relative recognition time have been tested.

Tests comparing the 83rd and the 84th versions of the ASR system have been made. Table 3 below presents results for the sub-set including randomly chosen statements, words from the validation set I 97 speakers.

Evaluation of speech recognition system for Polish 61 Table 5. The effect of optimization on the relative recognition time for the sub-set of validation set I 97 speakers. The results obtained using universal acoustic model without adaptation at 7 recognition quality levels available within the system are given.

It results from different content of the validation sets — they included statements of a different length. The average length of a statement for the validation set I is 13,1 s varying between 1,4 s and 81,9 s , while for validation set II the value is 5,1 s varying between 1,8 s and 17,7 s.

It should be mentioned that the optimization of recognition speed did not influence the recognition efficiency. Table 6. The effect of the optimization on the relative recognition time for the sub-set of validation set II 13 speakers. The effect of the optimization influence on the relative recognition time for the sub-set of validation set II 13 speakers. Comparison of recognition speed at different computers In order to asses recognition efficiency depending on capacity of computer used for speech recognition comparison tests have been carried out using the 84th version of the ASR system.

Evaluation of speech recognition system for Polish 63 Fig. The effect of relative time of recognition on speech accuracy for 7 quality recognition levels results for validation set I obtained using the universal acoustic model. The results proved that capacity of computer applied for speech recognition influences only the relative time of decoding, but it does not affect the recognition accuracy.

The effect of relative time of recognition on speech accuracy for 7 quality recognition levels results for validation set II obtained using the universal acoustic model.

Final results In order to evaluate the current level of recognition efficiency for the ASR system, quality and capacity tests have been carried out in December Table 6 and Figure 4 present recognition results accuracy, relative time and maximum accuracy for result word graph. Test were made using the 89 th version of the ASR system. Table 8. The effect of relative recognition time on recognition accuracy for 7 recognition quality levels.

The effect of relative time of recognition on recognition accuracy for 7 recognition quality levels. The results for validation set II 13 speakers obtained using the universal acoustic model without adaptation at 7 recognition quality levels are given. The effect of acoustic model adaptation to speaker voice on recognition efficiency and the effect of relative recognition time on recognition accuracy at 7 recognition quality levels.

The results for validation set II 13 speakers obtained using the adapted acoustic model with adaptation at 7 recognition quality levels are given. Tests made by the system users At the end of the Police tested the ASR system to prove automatic transcription efficiency and evaluate time gain obtained by typing text using the ASR system instead of a standard computer keyboard.

Sixteen police officers representing five Province Police Headquarters were provided with report texts, wrote them using the ASR system, recorded recognized texts, rewrote the reports on a computer, corrected both texts and estimated time needed for both activities. Police officers were also asked to use the ASR system in their daily tasks and activities related to legal proceedings and discovery processes. At the end of the tests the officers filled in the electronic version of the survey evaluating the ASR system, especially the interface, user friendliness, flow rate and recognition quality.

The survey results, however positive, showed unsatisfactory recognition time and accuracy. The tests proved that the ASR system efficiency resulting in time gain which depends mostly on the skills of the system use, proper dictating mode, individual approach and typing speed. Between June and October eight persons from various police units used the ASR system in their daily work. During tests the consultants sent comments to the system and proposals via discussion forums and questionnaires.

The comments were immediately included by creating new versions of the ASR system. Tests were carried out in diversified conditions: offices with different noise level, open-space offices, shopping centers, open space areas, near stadium area.

Very good results were achieved for office spaces, quite good results for shopping centers. Results for open space areas depended highly on conditions wind, noise.

During adjustment process based on test results the user interface has been modified, model reading has been accelerated, the requirements for software bit Windows installation possible , recognition process has been also accelerated especially longer statements. Many mistakes and errors reported by consultants have been deleted.

The persons who tested ASR submitted remarks on the system and adjustment proposals useful for transcription. Tests were carried out in various conditions single and double rooms. Transcriptionists listened to court case recordings of the length of minutes and of various quality and then typed the texts using the ASR system. Recording content was also wide- ranging a. We assume the integration of both systems would bring better results.

Vocabulary included in the system appeared sufficient, however jargon and some specialist vocabulary were not available jargon and language error annotation. Transcription in the ASR system for eight hours per day appeared inconvenient.

ASR should be treated as a support tool for court transcriptionists. Comparing to the tests from , the results obtained recently present a significant rise of positive evaluation made by prospect users in terms of system speed and its quality. Improvement in the evaluation of other aspects of the ASR system interface, user friendliness can also be observed.

References [1] Demenko G. Jurisdic—Polish Speech Database for taking dictation of legal texts. Development of large vocabulary continuous speech recognition using phonetically structured speech corpus. Springer for Research and Development. The work was carried out within a research-development project whose primary aim was to develop a speaker recognition and identification system mainly for forensic applications.

Introduction Contemporary speaker recognition and identification systems are often based on low-level, short-term frame spectral features, however, an increasing number of studies e. Forensic speaker identification requires a broad range of information including paralinguistic data which proved to be essential for certain methods of analysis [2].

At the same time, a number of constrictions need to be respected in the actual court practice e. Whereas the use of paralinguistic features as indicators of individual characteristics of voices is not questioned in general, it poses a number of problems when it comes to precise definitions of the features [4, 5, 6], distinguishing between them or developing systematic descriptions, tagging schemes or evaluation procedures that could be afterwards used in a repetitive way.

Analyses of voice features in general depend heavily on the availability of speech material, but for higher-level paralinguistic features this dependence appears to be even more important. A number of corpora used in speaker recognition or identification e. However, in order to investigate changes of these features over time, as well as to study the stability or individual changes of speech rhythm or speech rate it is necessary to use comparably long utterances composed of a number of phrases preferably more than just several phrases.

An important requirement is also the spontaneous or at least quasi-spontaneous character of speech material as well as its interactivity necessary to investigate voice features characteristic for real life conversations compare also [10].

One more feature whose investigation requires rather spontaneous and longer utterances is the individual choice and organization of vocabulary and the specificity of using syntactic structures. Although these features might be rather rarely taken into account in the context of automatic speaker recognition or identification, their role in the description of individual characteristics of a voice or a speaker should not be neglected.

Another issue is the vocal display of emotions and the relationships between paralinguistic features and affective states. The changeability of speaker’s emotional state is regarded as one of the more important difficulties to overcome in the development of automatic speaker recognizers e. Databases containing emotional speech can be divided in two types depending on whether they rely on acted or non- acted naturalistic speech in emotion-related states.

Databases of authentic emotional speech e. Reading-Leeds, Belfast or CREST-ESP databases described in [12] are characterized by high ecological validity, but they are less common than the acted ones mostly due to lack of control and some methodological problems e. Acted databases differ in the type of text material for the recordings: emotionally neutral sentences which can be used in everyday life [15] or nonsense, meaningless sentences [16, 17].

Some studies use affect bursts rather than speech [18]. Resources used for analyses of paralinguistic features and emotional speech can also be distinguished on the basis of the modalities that they include — they may consist only of one or more audio channels or they can provide both audio and visual information.

The use of multimodal databases is justified by their better correspondence to the multimodal character of human communication, however, for some applications using only audio channel might still appear sufficient as although in normal conditions the visual channel is an important factor, there are a range of real-life situations where speakers and listeners have to cope without it such as a simple conversation over the telephone see also [12].

The process of annotation, tagging and further feature extraction and modelling for spontaneous and emotionally marked speech data is yet another current challenge considering the fact that it is not always enough to simply adapt the methods used in more traditional domains of speech analysis and technology.

With some features, it proved to be useful to use graphic feature continuum or space representation instead of arbitrary assignment to categories or creating annotation specifications based on lists of tags and labels. In Section 2, the details concerning the database structure are presented. Section 3 describes the applied annotation tool and methodology. The preliminary results of analyses and experiments are presented in section 4 and further discussed in Section 5.

As a reference, apart from the target types of utterances i. The scenarios for the tasks were based on a prior analysis of police reports. Since a large number of the reports was related to descriptions of people or places buildings, rooms , it was decided to create recording scenarios involving these topics.

Each pair of speakers recorded four scenarios. In order to enable analysis of potential speech rate effects in dialogs, three of the scenarios included time restrictions. Each of the participants obtained one picture of a building. The pictures given to each person differed in a number of details. The task was to inform each other about the details seen in the pictures in order to find as many details as possible.

Scenario 1. Each of the participants was given exactly the same two pictures of a room with a number of differences.

The task was to co-operate with the interlocutor in order to find as many differences as possible in the shortest possible time the speakers were informed that the time needed for the realization of the task was measured.

One of the speakers was given a picture of a person, and was instructed to provide their interlocutor with as many features of the person’s appearance as possible while the other speaker’s task was to ask questions and ask for elaboration of the descriptions in order to learn about maximum number of details.

Both speakers were informed that the time of task completion is measured and that after their discussion there will be an additional task to perform based on the results of their discussion effectiveness of information exchange. Finally, after the discussion the person previously instructed to ask questions was presented a number of pictures of various people and was asked to identify the person described.

Scenario 2. The speaker whose role in Scenario 2. The other speaker was supposed to enquire about the details. Both speakers were informed that the time of task completion is measured and that after their discussion there will be another task to perform based on their conversation results.

However, in order to avoid repeating dialogue schemes from the previous scenario, it was emphasized that the second task was to be different than that in Scenario 2. Then, the person previously instructed to ask questions was asked to identify the person by choosing one out of a number of pictures of the same person but in different postures and using various hand or head gestures.

For each pair of speakers, one person was sitting in a university office room and their interlocutor was in an anechoic chamber, they had no eye contact. Dialogues were recorded using a multi-channel setup based on: in-built telephone recorders, head mounted close-talking microphones and video cameras.

Now I understand everything. Today is Monday. Od rana pada deszcz. It has been raining since morning. He said that nothing had happened. We are going on a trip to Greece. Altogether nine speakers were recorded including seven professional actors five females and two males and two non-professional male speakers. Beside definitions of emotions to be portrayed, actors were provided with two prototypical scenarios based on scenarios created for the GEMEP corpus, [17] to support the use of acting techniques such as Stanislawsky or Acting Method.

Three actors were engaged in the recordings and they acted two scenes presenting testimony of husband and wife before judge in a divorce case. The recordings were based on a script inspired by a pseudo- documentary TV court-show and on previous experience with original court case transcripts.

The script included detailed description of the situation as well as comments concerning emotional states of the participants. On the basis of the script the actors played a short scene. It can be assumed that the recorded speech material approx.

Moreover, it can also be expected that the expression of emotions was controlled by push and pull effects [23] and that the resulting speech is an adequate reflection of reality. Those parts of recordings in which voice of a police officer or any personal data appeared were removed.

Therefore the text of the utterances contained vocabulary effectively describing the ongoing situation, which was often emotionally loaded. Imitated speech material The imitators were asked to repeat phrases of each of the same sex speaker in a fashion as close to the original as possible prosodically, emotionally and lexically. Actors were also given the written script of each of the phrases that they were to imitate but were instructed not to read them while speaking.

The distance between mouth and microphone was controlled the actors were instructed not to change it significantly , but it was not constant. Moreover, the recording level had to be adjusted between very loud and very quiet speech, which makes reliable signal energy analysis difficult.

The material collected includes audio alone. As concerns actor portrayals of emotions, the speakers were instructed to avoid extreme realizations, i. Annotation software and methodology 3. With a view to enable annotation using continuous rating scales, a graphical representation of the feature space was designed and built in the software. Figure 1: Main window of the Annotation System displaying top to bottom : spectrogram, waveform and five annotation tiers corresponding to emotion categories, emotion dimensions top right corner , voice quality, prosody and phonetic alignment.

Users can use one of the pre-defined graphical representations or create one of their own in a simple format of. The results of the annotation are interpreted as Cartesian coordinates x, y which can be easily exported to a spreadsheet and statistically analyzed. Figure 1 shows the main window of the program; in the top right corner, an example graphical representation of valence and activation dimensions of emotions used in the perceptual analysis of speaker state can be seen.

All recordings included in the database have been orthographically transcribed. The recordings of dialogues have also been segmented into phrases pauses and changes in pitch contour were accepted as the main phrase boundary indicators , and additionally, noises and non-speech events have been marked using separate annotation tiers SPEECON-based classification [26].

Since the non-speech events classification is somewhat simplified and suitable rather to read or highly controlled speech there are just several markers to annotate all non- speech events and noises , it was decided to broaden the specification by adding new annotation tiers for annotation of paralinguistic features. Using “Paralingua” database for investigation of affective states … 79 Currently, the dialogues are annotated using graphical representations of scalar features for voice quality see 3.

Annotations consist in placing the cursor in the appropriate positions on the representation of the feature space. The result is a set of pairs of co-ordinates reflecting the perceived degree or intensity for particular features. A part of the sub-corpus of actor portrayals of emotion has already been annotated using graphical representations illustrating prosodic features, voice quality and emotions.

The resulting coordinates showing the appropriate areas on the feature space were saved in a text file and then exported to a spreadsheet and analysed see details in Sections 3.

Two sets of selected features have been used as a preliminary setting for perception-based assessment of voice quality. The feature sets have been represented in the form of graphic representations using pictures such as displayed in Figure 2. The features were selected to enable a perception-based description of voices with no serious disorders by both expert phoneticians and also naive listeners. Figure 2 a and b. Graphical representations used in the description of voice quality: asymmetric scale a, left and symmetric scale b, right.

Figure 2 a shows seven independent stripes used for annotation of voice quality features using continuous asymmetric scale ratings from minimum to maximum intensity of a feature without pre-defined categorization : nasal, reedy, weepy, dentalized, aspirated, lisping, laughing.

Between the center and the edge of the circle, a smaller circle was drawn which corresponded to unmarked value of a given feature, e. Preliminary annotation of perceived voice quality was also carried out using graphical representation Fig. On the basis of the phonetic voice quality description [27] the following qualities were distinguished: lax, tense, breathy, whispery, creaky, falsetto, harsh, modal and other. The middle of the circle corresponded to modal voice and the distance from the center to the edge indicated the intensity of a given voice quality increasing towards the edge.

Figure 3 a and b. Graphical representations used in the description of prosody a, to the left and voice quality b, to the right. One of them illustrated a circle divided by x and y coordinates into four areas corresponding to emotion dimensions of valence x axis and activation y axis, see also Figure 1. Emotions which belonged to the same family and differed only in activation intensity were collapsed into one category. Due to the assumed complexity of the profile and the expected cross-influences of a variety of factors it is generally intended to look at features related to various levels of analysis and types of features.

In this section, we summarize selected observations concerning the individual choice of lexical means in task-oriented dialogues, perception and production of affective speech, and the perceived reliability of imitated or mimic speech as compared to authentic emotional utterances. Altogether, the speakers produced phrases composed of above word realizations with repetitions, excluding non-finished or non-understandable items during the conversations in the four tasks.

However, the number of unique words within the whole set was only ca. After grouping the inflected forms into inflection paradigms it occurred that the total number of unique lemmas was What is worth noting is the very high frequency noted for quite colloquial expressions, e.

Maximum frequency of word occurrence per speaker the most frequent words are displayed as point labels The words used most frequently by particular speakers are shown in Figure 4, as it can be seen, six items appear as the top-frequent ones tak, no, jest, to, na, i, nie , for the majority of speakers the most often repeated words were related to a kind of confirmation or affirmative response, only for one person a negative particle nie appeared to be the most frequent.

The maximum frequency per speaker differed significantly, confirming individual variation in the tendencies in re-usage of the same vocabulary items. Speakers differed significantly in the overall word number used for the needs of the dialogue tasks realization. The total number of word realizations per speaker ranged from to , and for the unique items per speaker, the numbers ranged respectively from to The number of phrases per speaker also differed from to per person see Figure 5.

The correlation between the mean number of words and the mean number of phrases per speaker was also significant with the same p level, but it was a little weaker 0. For each phrase, the total duration was also measured, and the mean phrase durations per speaker appeared to be even more strongly correlated with the number of words: 0.

Using “Paralingua” database for investigation of affective states … 83 Figure 5. Word and phrase number per speaker, sorted by the unique word number. Blue solid bars – the count of unique vocabulary items, gradient – total number of words with repetitions, red – phrase number. The study aimed at investigation of the relationship between acoustic parameters and vocally expressed emotions and allowed to establish links between dimensional description of emotions and acoustic variables.

For this purpose a number of parameters in F0 and time domain were automatically extracted from the speech data using originally written Praat script. They included parameters commonly used in the literature [16], [22], [28], [29] : F0 mean, max, min, standard deviation of F0 in semitones and F0 range calculated as F0 max-F0 min and expressed in semitones.

F0 mean and the distribution of F0 max, mean and min were considered as correlates of pitch level and standard deviation of F0 — as a correlate of pitch variability. In time domain speech rate was measured it was expressed as the number of syllables per second , mean utterance length plus standard deviation from the mean , silence to speech ratio, and four parameters related to duration of voiced and unvoiced intervals details are given in [30], this volume.

Some emotions are expressed using distinctive patterns of F0 level, span and variability e. The effect of emotion on features in time domain is smaller comparing to F0 domain. The most important feature in time domain, i. There are significant differences in the use of pitch level variation not only between emotions, but also between speakers, which may signal that speakers use various strategies to communicate emotions vocally. On the basis of the production study it was possible to establish patterns of variability in selected prosodic features which characterize vocal expression of the twelve emotions.

In the future it is planned to include in the analyses also features related to voice quality and articulation precision and to use different measures of pitch variability and different ways of normalization in both F0 and time domain.

The production study investigated also the relationship between selected acoustic features and emotions dimensions of valence and activation details are given in [30], this volume. It was found that positive valence is associated with slower speech rate, higher variability in duration of voiced intervals and greater pitch variability.

As regards activation, there is a strong positive relationship between speech rate and activation faster speech rate is linked to higher activation. High activation is also signaled by higher pitch level, more variable pitch contours and greater pitch span. Using “Paralingua” database for investigation of affective states … 85 4. The source material for imitation was recorded in CCIT A-law format with 8 kHz sampling rate, yet the characteristics of each of the telephone microphones where unknown and most likely different for each of the speakers.

Therefore, the recordings where rich in background noises and differed in terms of their frequency spectra. To make the imitation material sound closer to the source material, in the first step the imitation recordings were transformed into A-law format using SoX – Sound eXchange software. In the next step using Audacity software some background noises from the source recordings were mixed into corresponding imitation recordings and the spectra of imitation recordings where manually adjusted in Audacity graphic equalizer to resemble their corresponding source recordings.

The Annotation System’s perception test interface has been used as software environment. The responses were marked by the participants by clicking on the graphical representations. The obtained co-ordinates were saved to an XML file one for each of the listeners and then exported to a spreadsheet. Graphical representations used in the perception test: emotion category wheel in Task 1 a, to the left , assessment of imitation likelihood, control and reliability in Task 2 b, to the right.

As a result, 4 out of 8 authentic recordings one for each caller were rated by the expert with lower intensity than the other 4 recordings. In Task 1 listeners confirmed this evaluation. The graphical representations used in Task 1 have been constructed in such a way that negative emotions are plotted on the left hemisphere of the wheel and the positive emotions on its right hemisphere except for the other category which is undefined. Additionally, the bottom part of the wheel contains emotions with lower activation than the upper part.

Figure 7 displays scatterplots of perception test results for imitated top and authentic speech bottom. Comparison of the wheel Figure 6a with the scatterplots showed that neither the ratings for authentic speech nor for imitated speech, were plotted on positive, low activity emotion, only few were plotted on other positive emotions and the rest of the ratings were plotted on the negative emotions.

Using “Paralingua” database for investigation of affective states … 87 Figure 7: Scatterplots of perception test results for imitated top and authentic bottom speech in Task 1 4. The dimensions: probable-unlikely In the context of a given situation, how probable was the emotional reaction heard in the recording. Certain bimodality appeared in the distribution of controlled-spontanous dimension ratings for authentic recordings.

This might be related to emotion intensity evaluation — 4 utterances were given high intensity notes, and intense emotions are difficult to suppress and control therefore seem more spontaneous, whereas 4 utterances were given lower intensity ratings related to more controlled expressions of emotions.

Worth noting is the fact that for one of the imitators who was actually not a professional actor the ratings were in a few cases higher than for the professional actors. Apart from audio recordings, the task-oriented dialogue sub- corpus includes also video recordings.

As one of the further tasks, it is intended to incorporate the visual materials into the analyses of paralinguistic features. The annotation specifications and a newly developed software platform have been preliminarily tested and will be subject of further inspection and verification in the future. The preliminary analysis of the choice of lexical means and phrasing in the task-oriented dialogue section confirmed the individual differentiation in the size of the word inventory, as well as the speaker-dependent tendencies in re-usage of the same vocabulary items.

Using “Paralingua” database for investigation of affective states … 89 In the production study based on the actor portrayals of emotions, the relationship between acoustic features on one hand and emotions and emotion dimensions on the other were investigated. Analysis of distribution of duration and F0 features and results of factorial ANOVA made it possible to identify vocal patterning in the expression of emotions.

The comparison of perceptual assessments of imitated versus authentic emotional speech showed certain similarities between the two types of emotion expression material.

Especially interesting was the fact that the evaluation of non-actor imitations did not differ significantly from the actor imitations and in a few cases it was evaluated even closer to the authentic expression than for the professional actor imitation.

Further research and work on this type of data should include more non-actor imitations, as well as broadening the range of emotion expressions in both authentic recordings and their imitations. Acknowledgements 1. This work has been supported from the financial resources for science in the years — as a development project project no.

O R00 References [1] Shriberg, E. In: C. Muller Ed. Speaker Classification I. Heidelberg, Berlin, New York: Springer. Jones and Bartlett Publishers. Speaker identification evidence: its forms, limitations, and roles. Retrieved 15 Dec. Lingua Posnaniensis, LIV 2 , pp. DOI The Interspeech paralinguistic challenge. Interspeech pp.

The Interspeech speaker state challenge. ISCA YOHO speaker verification. Linguistic Data Consortium, Philadelphia. Globalphone: a multilingual speech and text database developed at Karlsruhe University. ICSLP pp. Speaker Recognition. In Cole, R. Survey of the state of the art in human language technology pp. Cambridge University Press. Emotional speech: Towards a new generation of databases. Speech communication, 40 1 , Describing the emotional states that are expressed in speech.

Speech Communication, 40 1 , Vocal communication of emotion: A review of research paradigms. A database of german emotional speech. Interspeech Vol. Acoustic profiles in vocal emotion expression. Journal of personality and social psychology, 70 3 , Affective computing and intelligent interaction, Experimental study of affect bursts. An argument for basic emotions. A circumplex model of affect. Journal of personality and social psychology, 39 6 , The Journal of the Acoustical Society of America, , Emotional expression in prosody: a review and an agenda for future research.

In Speech Prosody , International Conference. Authentic and play-acted vocal emotion expressions reveal acoustic differences. Frontiers in Psychology, 2. Specification of Databases – Specification of annotation. Cambridge Studies in Linguistics. An acoustic study of emotions expressed in speech. Acoustical correlates of affective prosody. Journal of Voice, 21 5 , Speech and Languages Technology, vol. Research workers have searched for phonetic and acoustic tips included in speech that would indicate being in the state of intoxication or sobriety.

The aim of this work is to present the results based on acoustic and linguistic analysis of recordings of the speech under the influence of alcohol. All recordings are selected from the alcohol database that was created on the need of POP project [1]. The paper is organized as follows: Chapter one contains the brief information of alcohol as a substance and it presents previous research about the effect of alcohol on speech production. Chapter two illustrates the description of the alcohol database created on the need of POP project.

Chapter three is associated with the results of linguistic analysis based on recordings from the alcohol database. It also briefly presents the preliminary measurements of acoustic features. The last chapter summarizes obtained results and explains problems that appeared while conducting the research. Introduction Alcohol is a small molecule that passes easily through membranes of the body. The kind of consumed food, beverages or other condiments influences on the level of absorption of alcohol A tiny dose of alcohol may effect very positively on the body.

Some cases show that it improves psychomotor movements, makes someone relax and calm. On the other side, a large amount of alcohol could cause complete unbalance in psychomotor movements, nystagmus, lack of attention, memory losses, increased aggression or quite opposite — amusement. Over the years many studies of speech under the influence of alcohol have been carried out.

One of the most famous is the analysis of speech of the captain of the oil tanker that run aground in Prince William Sound on March The captain was suspected of being under the influence of alcohol when the accident happened.

The National Transportation Safety Board engaged two independent groups of scientists to determine whether recordings of radio transmissions between the captain and the Coast Guard Traffic Center indicate being intoxicated.

The methodology and results of the analysis are detailed in many articles and books [2], [3], [4]. The analysis of speech mentioned above is called the case study where experimenters carry out research on the base of existing recordings.

Very often, scientists have also done clinical research on alcoholics who are in hospitals, care institutions or clinics. Beam et al. Collins [6] examined the syntactic and semantic possibilities of 39 alcoholic patients and compared the results to a control group consisting of 39 sober people. Niedzielska et al. They took into account the difference in the length of the addiction and compared the results to a control group.

So far, the largest number of experiments have been carried out on volunteers who are intoxicated by alcohol. Trojan and Kryspin – Exner [8] examined production of speech before and after consumption of alcohol of three male volunteers. Zimov [9] involved twenty people who took writing and reading test while being intoxicated.

Smith et al. This study included a wide range of analysis: linguistic, phonetic and phoniatric. Automatic detection of alcohol based on human speech is nowadays widely experienced. Researchers are trying to find characteristic features in speech that indicate intoxication. Many scientific groups create or assemble databases that include speech under the influence of alcohol. The example of such activities is an experiment that was carried out by research workers from Germany [13].

Their aim was to detect alcoholization in recordings from Police Academy in Hessen. In this experiment, the voice signal was cut into phrasal units that last 2. Based on these specific units such prosodic features as fundamental frequency, energy and zero crossing rate were measured. It was assumed that prosodic features mentioned above would be the best indicator of intoxication. All assembled records were divided into two classes: alcoholized and not alcoholized for the purpose of training and classification.

The results show that it is hard to detect the influence of alcohol in speech when the alcohol blood level varying between 0. The influence of alcohol is one of the major causes of traffic accidents. The main problem is the fact that it is only randomly detected during police controls or after the accident, where in most cases the drivers turned out to be drunk. To achieve this goal it is necessary to find out which prosodic parameters of speech indicate the influence of alcohol.

Before describing more precisely the experiment it should be noted that this experiment was carried out on the base of recordings from Alcohol Language Corpus ALC [15]. ALC is a speech corpus of male and female speakers. It contains speech of sober and intoxicated speakers that were recorded in the automotive environment. If someone is interested in analyzing speech under the influence of alcohol the ALC corpus is available for unrestricted scientific and commercial usage. More information could be found in articles [14, 15].

From a variety of contents and speech styles only command and control speech were chosen telephone numbers, credit card numbers, addresses, city names, read commands, spontaneous commands. On the base of these recordings, fundamental frequency and rhythm analysis was done. It was found out that values of F0 rise when speakers are intoxicated. Rhythm parameter also revealed significant changes in speech under the influence of alcohol.

But these changes did not show stable tendency and depended on speech style read vs. The accurate results are shown in the article [14].

Database design The alcohol database was recorded over a time period of 3 days. In according to length of creating other databases it is a great success. This success would not be achieved without voluntary speakers and excellent work of people who recorded the speech. Speakers Database contains speech recordings of 31 sober and intoxicated speakers aged between 20 and 60 10 women, 21 men.

Their first language is Polish. None of the speakers has speech impediments. Recorded speech The alcohol database includes a variety of speech styles: read and spontaneous. Recording types are shown in Table 1 below. Table 1. In the read style, participants were asked to utter vowels, words, phrases, sentences in a loud but not shouted and more silently way.

The aim of this task was to get the speech at two different levels of vocal effort higher and lower. What is more, the words that had to be uttered were specially chosen. They represented minimal pairs that differ only in one sound e. The alcohol test was supervised and conducted by police officers with a specialist breathalyzer. Three persons were responsible for the speech recordings. Each of them was located in a separate room and stayed sober over a time of creating the alcohol database.

After drinking the specific dose of alcohol and being tested with the breathalyzer speakers returned to the same room where they were once recorded in. Annotation To annotate and tag all recordings Praat programme was used.

During this process eight tiers were specified. Table 2 shows the name of tiers and particular tags that concern speakers and linguistic errors. Table 2. In both cases the exact text were just written down. In the Picture 1 there is an example of Praat window. Preliminary evaluation of the alcohol database 99 Figure 1.

An example of Praat window 3. Measurements and results Because of the fact that the alcohol database includes so many speech styles with a variety of speech forms only vowels, picture descriptions and refusing to sign traffic tickets were chosen. The reason why vowels were taken into consideration is the fact that they are the best transmitter of nonlinguistic information.

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