A continuous speech recognition system using phonotactic constraints

This paper describes a speaker-independent recognition system for continuous German speech based on semicontinuous Hidden-Markov-Models which produces a phonetic transcription of the spoken sentence. The recognition units are parts of syllables while the output is a phoneme level transcription. During recognition, the phonotactic constraints of German are taken into account by a micro syntax constrained Viterbi algorithm. A maximum likelihood training procedure based on Viterbi training together with a simple but efficient seed model generation algorithm is presented

Hidden Markov models for gesture recognition

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 41-42).by Donald O. Tanguay, Jr.M.Eng

Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition

A theoretical framework for Bayesian adaptive training of the parameters of a discrete hidden Markov model (DHMM) and of a semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posteriori) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally efficient segmental quasi-Bayes algorithm for estimating the state-specific mixture coefficients in SCHMM is developed. For estimating the parameters of the prior densities, a new empirical Bayes method based on the moment estimates is also proposed. The MAP algorithms and the prior parameter specification are directly applicable to training speaker adaptive HMMs. Practical issues related to the use of the proposed techniques for HMM-based speaker adaptation are studied. The proposed MAP algorithms are shown to be effective especially in the cases in which the training or adaptation data are limited.published_or_final_versio

A continuous speech recognition system using phonotactic constraints

This paper describes a speaker-independent recognition system for continuous German speech based on semicontinuous Hidden-Markov-Models which produces a phonetic transcription of the spoken sentence. The recognition units are parts of syllables while the output is a phoneme level transcription. During recognition, the phonotactic constraints of German are taken into account by a micro syntax constrained Viterbi algorithm. A maximum likelihood training procedure based on Viterbi training together with a simple but efficient seed model generation algorithm is presented

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

On-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate

We present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.published_or_final_versio

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

Implementation and Evaluation of Acoustic Distance Measures for Syllables

Munier C. Implementation and Evaluation of Acoustic Distance Measures for Syllables. Bielefeld (Germany): Bielefeld University; 2011.In dieser Arbeit werden verschiedene akustische Ähnlichkeitsmaße für Silben motiviert und anschließend evaluiert. Der Mahalanobisabstand als lokales Abstandsmaß für einen Dynamic-Time-Warping-Ansatz zum Messen von akustischen Abständen hat die Fähigkeit, Silben zu unterscheiden. Als solcher erlaubt er die Klassifizierung von Silben mit einer Genauigkeit, die für die Klassifizierung von kleinen akustischen Einheiten üblich ist (60 Prozent für eine Nächster-Nachbar-Klassifizierung auf einem Satz von zehn Silben für Samples eines einzelnen Sprechers). Dieses Maß kann durch verschiedene Techniken verbessert werden, die jedoch seine Ausführungsgeschwindigkeit verschlechtern (Benutzen von mehr Mischverteilungskomponenten für die Schätzung von Kovarianzen auf einer Gaußschen Mischverteilung, Benutzen von voll besetzten Kovarianzmatrizen anstelle von diagonalen Kovarianzmatrizen). Durch experimentelle Evaluierung wird deutlich, dass ein gut funktionierender Algorithmus zur Silbensegmentierung, welcher eine akkurate Schätzung von Silbengrenzen erlaubt, für die korrekte Berechnung von akustischen Abständen durch die in dieser Arbeit entwickelten Ähnlichkeitsmaße unabdingbar ist. Weitere Ansätze für Ähnlichkeitsmaße, die durch ihre Anwendung in der Timbre-Klassifizierung von Musikstücken motiviert sind, zeigen keine adäquate Fähigkeit zur Silbenunterscheidung.In this work, several acoustic similarity measures for syllables are motivated and successively evaluated. The Mahalanobis distance as local distance measure for a dynamic time warping approach to measure acoustic distances is a measure that is able to discriminate syllables and thus allows for syllable classification with an accuracy that is common to the classification of small acoustic units (60 percent for a nearest neighbor classification of a set of ten syllables using samples of a single speaker). This measure can be improved using several techniques that however impair the execution speed of the distance measure (usage of more mixture density components for the estimation of covariances from a Gaussian mixture model, usage of fully occupied covariance matrices instead of diagonal covariance matrices). Through experimental evaluation it becomes evident that a decently working syllable segmentation algorithm allowing for accurate syllable border estimations is essential to the correct computation of acoustic distances by the similarity measures developed in this work. Further approaches for similarity measures which are motivated by their usage in timbre classification of music pieces do not show adequate syllable discrimination abilities