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

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Computer lipreading via hybrid deep neural network hidden Markov models

Constructing a viable lipreading system is a challenge because it is claimed that only 30% of information of speech production is visible on the lips. Nevertheless, in small vocabulary tasks, there have been several reports of high accuracies. However, investigation of larger vocabulary tasks is rare. This work examines constructing a large vocabulary lipreading system using an approach based-on Deep Neural Network Hidden Markov Models (DNN-HMMs). We present the historical development of computer lipreading technology and the state-ofthe-art results in small and large vocabulary tasks. In preliminary experiments, we evaluate the performance of lipreading and audiovisual speech recognition in small vocabulary data sets. We then concentrate on the improvement of lipreading systems in a more substantial vocabulary size with a multi-speaker data set. We tackle the problem of lipreading an unseen speaker. We investigate the effect of employing several stepstopre-processvisualfeatures. Moreover, weexaminethecontributionoflanguage modelling in a lipreading system where we use longer n-grams to recognise visual speech. Our lipreading system is constructed on the 6000-word vocabulary TCDTIMIT audiovisual speech corpus. The results show that visual-only speech recognition can definitely reach about 60% word accuracy on large vocabularies. We actually achieved a mean of 59.42% measured via three-fold cross-validation on the speaker independent setting of the TCD-TIMIT corpus using Deep autoencoder features and DNN-HMM models. This is the best word accuracy of a lipreading system in a large vocabulary task reported on the TCD-TIMIT corpus. In the final part of the thesis, we examine how the DNN-HMM model improves lipreading performance. We also give an insight into lipreading by providing a feature visualisation. Finally, we present an analysis of lipreading results and suggestions for future development

Automatic Proficiency Evaluation of Spoken English by Japanese Learners for Dialogue-Based Language Learning System Based on Deep Learning

Tohoku University伊藤彰則課

Exploring Speech Technologies for Language Learning

The teaching of the pronunciation of any foreign language must encompass both segmental and suprasegmental aspects of speech. In computational terms, the two levels of language learning activities can be decomposed at least into phonemic aspects, which include the correct pronunciation of single phonemes and the co-articulation of phonemes into higher phonological units; as well as prosodic aspects which include  the correct position of stress at word level;  the alternation of stress and unstressed syllables in terms of compensation and vowel reduction;  the correct position of sentence accent;  the generation of the adequate rhymth from the interleaving of stress, accent, and phonological rules;  the generation of adequate intonational pattern for each utterance related to communicative functions; As appears from above, for a student to communicate intelligibly and as close as possible to native-speaker's pronunciation, prosody is very important [3]. We also assume that an incorrect prosody may hamper communication from taking place and this may be regarded a strong motivation for having the teaching of Prosody as an integral part of any language course. From our point of view it is much more important to stress the achievement of successful communication as the main objective of a second language learner rather than the overcoming of what has been termed “foreign accent”, which can be deemed as a secondary goal. In any case, the two goals are certainly not coincident even though they may be overlapping in some cases. We will discuss about these matter in the following sections. All prosodic questions related to “rhythm” will be discussed in the first section of this chapter. In [4] the author argues in favour of prosodic aids, in particular because a strong placement of word stress may impair understanding from the listener’s point of view of the word being pronounced. He also argues in favour of acquiring correct timing of phonological units to overcome the impression of “foreign accent” which may ensue from an incorrect distribution of stressed vs. unstressed stretches of linguistic units such as syllables or metric feet. Timing is not to be confused with speaking rate which need not be increased forcefully to give the impression of a good fluency: trying to increase speaking rate may result in lower intelligibility. The question of “foreign accent” is also discussed at length in (Jilka M., 1999). This work is particularly relevant as far as intonational features of a learner of a second language which we will address in the second section of this chapter. Correcting the Intonational Foreign Accent (hence IFA) is an important component of a Prosodic Module for self-learning activities, as categorical aspects of the intonation of the two languages in contact, L1 and L2 are far apart and thus neatly distinguishable. Choice of the two languages in contact is determined mainly by the fact that the distance in prosodic terms between English and Italian is maximal, according to (Ramus, F. and J. Mehler, 1999; Ramus F., et al., 1999)

Subspace Gaussian Mixture Models for Language Identification and Dysarthric Speech Intelligibility Assessment

En esta Tesis se ha investigado la aplicación de técnicas de modelado de subespacios de mezclas de Gaussianas en dos problemas relacionados con las tecnologías del habla, como son la identificación automática de idioma (LID, por sus siglas en inglés) y la evaluación automática de inteligibilidad en el habla de personas con disartria. Una de las técnicas más importantes estudiadas es el análisis factorial conjunto (JFA, por sus siglas en inglés). JFA es, en esencia, un modelo de mezclas de Gaussianas en el que la media de cada componente se expresa como una suma de factores de dimensión reducida, y donde cada factor representa una contribución diferente a la señal de audio. Esta factorización nos permite compensar nuestros modelos frente a contribuciones indeseadas presentes en la señal, como la información de canal. JFA se ha investigado como clasficador y como extractor de parámetros. En esta última aproximación se modela un solo factor que representa todas las contribuciones presentes en la señal. Los puntos en este subespacio se denominan i-Vectors. Así, un i-Vector es un vector de baja dimensión que representa una grabación de audio. Los i-Vectors han resultado ser muy útiles como vector de características para representar señales en diferentes problemas relacionados con el aprendizaje de máquinas. En relación al problema de LID, se han investigado dos sistemas diferentes de acuerdo al tipo de información extraída de la señal. En el primero, la señal se parametriza en vectores acústicos con información espectral a corto plazo. En este caso, observamos mejoras de hasta un 50% con el sistema basado en i-Vectors respecto al sistema que utilizaba JFA como clasificador. Se comprobó que el subespacio de canal del modelo JFA también contenía información del idioma, mientras que con los i-Vectors no se descarta ningún tipo de información, y además, son útiles para mitigar diferencias entre los datos de entrenamiento y de evaluación. En la fase de clasificación, los i-Vectors de cada idioma se modelaron con una distribución Gaussiana en la que la matriz de covarianza era común para todos. Este método es simple y rápido, y no requiere de ningún post-procesado de los i-Vectors. En el segundo sistema, se introdujo el uso de información prosódica y formántica en un sistema de LID basado en i-Vectors. La precisión de éste estaba por debajo de la del sistema acústico. Sin embargo, los dos sistemas son complementarios, y se obtuvo hasta un 20% de mejora con la fusión de los dos respecto al sistema acústico solo. Tras los buenos resultados obtenidos para LID, y dado que, teóricamente, los i-Vectors capturan toda la información presente en la señal, decidimos usarlos para la evaluar de manera automática la inteligibilidad en el habla de personas con disartria. Los logopedas están muy interesados en esta tecnología porque permitiría evaluar a sus pacientes de una manera objetiva y consistente. En este caso, los i-Vectors se obtuvieron a partir de información espectral a corto plazo de la señal, y la inteligibilidad se calculó a partir de los i-Vectors obtenidos para un conjunto de palabras dichas por el locutor evaluado. Comprobamos que los resultados eran mucho mejores si en el entrenamiento del sistema se incorporaban datos de la persona que iba a ser evaluada. No obstante, esta limitación podría aliviarse utilizando una mayor cantidad de datos para entrenar el sistema.In this Thesis, we investigated how to effciently apply subspace Gaussian mixture modeling techniques onto two speech technology problems, namely automatic spoken language identification (LID) and automatic intelligibility assessment of dysarthric speech. One of the most important of such techniques in this Thesis was joint factor analysis (JFA). JFA is essentially a Gaussian mixture model where the mean of the components is expressed as a sum of low-dimension factors that represent different contributions to the speech signal. This factorization makes it possible to compensate for undesired sources of variability, like the channel. JFA was investigated as final classiffer and as feature extractor. In the latter approach, a single subspace including all sources of variability is trained, and points in this subspace are known as i-Vectors. Thus, one i-Vector is defined as a low-dimension representation of a single utterance, and they are a very powerful feature for different machine learning problems. We have investigated two different LID systems according to the type of features extracted from speech. First, we extracted acoustic features representing short-time spectral information. In this case, we observed relative improvements with i-Vectors with respect to JFA of up to 50%. We realized that the channel subspace in a JFA model also contains language information whereas i-Vectors do not discard any language information, and moreover, they help to reduce mismatches between training and testing data. For classification, we modeled the i-Vectors of each language with a Gaussian distribution with covariance matrix shared among languages. This method is simple and fast, and it worked well without any post-processing. Second, we introduced the use of prosodic and formant information with the i-Vectors system. The performance was below the acoustic system but both were found to be complementary and we obtained up to a 20% relative improvement with the fusion with respect to the acoustic system alone. Given the success in LID and the fact that i-Vectors capture all the information that is present in the data, we decided to use i-Vectors for other tasks, specifically, the assessment of speech intelligibility in speakers with different types of dysarthria. Speech therapists are very interested in this technology because it would allow them to objectively and consistently rate the intelligibility of their patients. In this case, the input features were extracted from short-term spectral information, and the intelligibility was assessed from the i-Vectors calculated from a set of words uttered by the tested speaker. We found that the performance was clearly much better if we had available data for training of the person that would use the application. We think that this limitation could be relaxed if we had larger databases for training. However, the recording process is not easy for people with disabilities, and it is difficult to obtain large datasets of dysarthric speakers open to the research community. Finally, the same system architecture for intelligibility assessment based on i-Vectors was used for predicting the accuracy that an automatic speech recognizer (ASR) system would obtain with dysarthric speakers. The only difference between both was the ground truth label set used for training. Predicting the performance response of an ASR system would increase the confidence of speech therapists in these systems and would diminish health related costs. The results were not as satisfactory as in the previous case, probably because an ASR is a complex system whose accuracy can be very difficult to be predicted only with acoustic information. Nonetheless, we think that we opened a door to an interesting research direction for the two problems

Rapid Generation of Pronunciation Dictionaries for new Domains and Languages

This dissertation presents innovative strategies and methods for the rapid generation of pronunciation dictionaries for new domains and languages. Depending on various conditions, solutions are proposed and developed. Starting from the straightforward scenario in which the target language is present in written form on the Internet and the mapping between speech and written language is close up to the difficult scenario in which no written form for the target language exists