Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

Speaker normalisation for large vocabulary multiparty conversational speech recognition

One of the main problems faced by automatic speech recognition is the variability of the testing conditions. This is due both to the acoustic conditions (different transmission channels, recording devices, noises etc.) and to the variability of speech across different speakers (i.e. due to different accents, coarticulation of phonemes and different vocal tract characteristics). Vocal tract length normalisation (VTLN) aims at normalising the acoustic signal, making it independent from the vocal tract length. This is done by a speaker specific warping of the frequency axis parameterised through a warping factor. In this thesis the application of VTLN to multiparty conversational speech was investigated focusing on the meeting domain. This is a challenging task showing a great variability of the speech acoustics both across different speakers and across time for a given speaker. VTL, the distance between the lips and the glottis, varies over time. We observed that the warping factors estimated using Maximum Likelihood seem to be context dependent: appearing to be influenced by the current conversational partner and being correlated with the behaviour of formant positions and the pitch. This is because VTL also influences the frequency of vibration of the vocal cords and thus the pitch. In this thesis we also investigated pitch-adaptive acoustic features with the goal of further improving the speaker normalisation provided by VTLN. We explored the use of acoustic features obtained using a pitch-adaptive analysis in combination with conventional features such as Mel frequency cepstral coefficients. These spectral representations were combined both at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA), and at the system level using ROVER. We evaluated this approach on a challenging large vocabulary speech recognition task: multiparty meeting transcription. We found that VTLN benefits the most from pitch-adaptive features. Our experiments also suggested that combining conventional and pitch-adaptive acoustic features using HLDA results in a consistent, significant decrease in the word error rate across all the tasks. Combining at the system level using ROVER resulted in a further significant improvement. Further experiments compared the use of pitch adaptive spectral representation with the adoption of a smoothed spectrogram for the extraction of cepstral coefficients. It was found that pitch adaptive spectral analysis, providing a representation which is less affected by pitch artefacts (especially for high pitched speakers), delivers features with an improved speaker independence. Furthermore this has also shown to be advantageous when HLDA is applied. The combination of a pitch adaptive spectral representation and VTLN based speaker normalisation in the context of LVCSR for multiparty conversational speech led to more speaker independent acoustic models improving the overall recognition performances

An analysis-by-synthesis approach to vocal tract modeling for robust speech recognition

In this thesis we present a novel approach to speech recognition that incorporates knowledge of the speech production process. The major contribution is the development of a speech recognition system that is motivated by the physical generative process of speech, rather than the purely statistical approach that has been the basis for virtually all current recognizers. We follow an analysis-by-synthesis approach. We begin by attributing a physical meaning to the inner states of the recognition system pertaining to the configurations the human vocal tract takes over time. We utilize a geometric model of the vocal tract, adapt it to our speakers, and derive realistic vocal tract shapes from electromagnetic articulograph (EMA) measurements in the MOCHA database. We then synthesize speech from the vocal tract configurations using a physiologically-motivated articulatory synthesis model of speech generation. Finally, the observation probability of the Hidden Markov Model (HMM) used for phone classification is a function of the distortion between the speech synthesized from the vocal tract configurations and the real speech. The output of each state in the HMM is based on a mixture of density functions

Automatic assessment of motivational interview with diabetes patients

Diabetes cost the UK NHS £10 billion each year, and the cost pressure is projected to get worse. Motivational Interviewing (MI) is a goal-driven clinical conversation that seeks to reduce this cost by encouraging patients to take ownership of day-to-day monitoring and medication, whose effectiveness is commonly evaluated against the Motivational Interviewing Treatment Integrity (MITI) manual. Unfortunately, measuring clinicians’ MI performance is costly, requiring expert human instructors to ensure the adherence of MITI. Although it is desirable to assess MI in an automated fashion, many challenges still remain due to its complexity. In this thesis, an automatic system to assess clinicians adherence to the MITI criteria using different spoken language techniques was developed. The system tackled the chal- lenges using automatic speech recognition (ASR), speaker diarisation, topic modelling and clinicians’ behaviour code identification. For ASR, only 8 hours of in-domain MI data are available for training. The experiments with different open-source datasets, for example, WSJCAM0 and AMI, are presented. I have explored adaptative training of the ASR system and also the best training criterion and neural network structure. Over 45 minutes of MI testing data, the best ASR system achieves 43.59% word error rate. The i-vector based diarisation system achieves an F-measure of 0.822. The MITI behaviour code classification system with manual transcriptions achieves an accuracy of 78% for Non Question/Question classification, an accuracy of 80% for Open Question/Closed Question classification and an accuracy of 78% for MI Adherence and MI Non-Adherence classification. Topic modelling was applied to track whether the conversation segments were related to ‘diabetes’ or not on manual transcriptions as well as ASR outputs. The full automatic assessment system achieve an Assessment Error Rate of 22.54%. This is the first system that targets the full automation of MI assessment with reasonable performance. In addition, the error analysis from each step is able to guide future research in this area for further improvement and optimisation

Speaker recognition: current state and experiment

[ANGLÈS] In this thesis the operation of the speaker recognition systems is described and the state of the art of the main working blocks is studied. All the research papers looked through can be found in the References. As voice is unique to the individual, it has emerged as a viable authentication method. There are several problems that should be considered as the presence of noise in the environment and changes in the voice of the speakers due to sickness for example. These systems combine knowledge from signal processing for the feature extraction part and signal modeling for the classification and decision part. There are several techniques for the feature extraction and the pattern matching blocks, so it is quite tricky to establish a unique and optimum solution. MFCC and DTW are the most common techniques for each block, respectively. They are discussed in this document, with a special emphasis on their drawbacks, that motivate new techniques which are also presented here. A search through the Internet is done in order to find commercial working implementations, which are quite rare, then a basic introduction to Praat is presented. Finally, some intra-speaker and inter-speaker tests are done using this software.[CASTELLÀ] En esta tesis, el funcionamento de los sistemas de reconocimiento del hablante es descrito y el estado del arte de los principales bloques de funcionamento es estudiado. Todos los documentos de investigación consultados se encuentran en las referencias. Dado que la voz es única al individuo, se ha vuelto un método viable de identificación. Hay varios problemas que han de ser considerados, como la presencia de ruido en el ambiente y los cambios en la voz de los hablantes, por ejemplo debido a enfermedades. Estos sistemas combinan conocimiento de procesado de señal en la parte de extracción de características de la voz y modelaje de señal en la parte de clasificación y decisión. Hay diferentes técnicas para la extracción de las características, y para el tratamiento de la similitud entre patrones, de tal manera que es complicado establecer una única y óptima solución. MFCC y DTW son las técnicas más comunes para cada bloque, respectivamente. Son tratadas en este documento, haciendo énfasis en sus problemas, que motivan nuevas técnicas, que también son presentadas aquí. Se realiza una búsqueda por Internet, para encontrar productos comerciales implementados, que son pocos, posteriormente se hace una introducción al software Praat. Finalmente, se realizan algunos intra-speaker i inter-speaker tests usando este programa.[CATALÀ] En aquesta tesi, el funcionament dels sistemes de reconeixement del parlant és descrit i l'estat de l'art dels principals blocs de funcionament és estudiat. Tots els documents de recerca consultats es troben a les referències. Donat que la veu és única a l'individu, ha esdevingut un mètode viable d'identificació. Hi ha diversos problemes que han de ser considerats, com ara la presència de soroll en l'ambient i els canvis en la veu dels parlants, per exemple deguts a malalties. Aquests sistemes combinen coneixement de processament de senyal en la part d'extracció de característiques de la veu i modelatge de senyal en la part de classificació i decisió. Hi ha diferents tècniques per a l'extracció de les característiques, i per al tractament de la similitud entre patrons, de tal manera que és complicat establir una única i òptima solució. MFCC i DTW són les tècniques més comunes per a cada bloc, respectivament. Són tractades en aquest document, fent èmfasi en els seus problemes, que motiven noves tècniques, que també són presentades aquí. Es realitza una cerca per Internet, per tal de trobar productes comercials implementats, que són pocs, posteriorment es fa una introducció al software Praat. Finalment, es realitzen alguns intra-speaker i inter-speaker tests fent servir aquest programa

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes