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

A large vocabulary speech recognition system for Turkish

Ankara : Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent University, 1999.Thesis (Master's) -- Bilkent University, 1999.Includes bibliographical references leaves 56-58.Yılmaz, CemalM.S

Hidden Markov models and neural networks for speech recognition

The Hidden Markov Model (HMMs) is one of the most successful modeling approaches for acoustic events in speech recognition, and more recently it has proven useful for several problems in biological sequence analysis. Although the HMM is good at capturing the temporal nature of processes such as speech, it has a very limited capacity for recognizing complex patterns involving more than first order dependencies in the observed data sequences. This is due to the first order state process and the assumption of state conditional independence between observations. Artificial Neural Networks (NNs) are almost the opposite: they cannot model dynamic, temporally extended phenomena very well, but are good at static classification and regression tasks. Combining the two frameworks in a sensible way can therefore lead to a more powerful model with better classification abilities. The overall aim of this work has been to develop a probabilistic hybrid of hidden Markov models and neural networks and ..

Advances and trends in automatic speech recognition

This paper aimts at giving an overview of récent advances in the domain of Speech Recognition . The paper mainly focttses on Speech Recognition, but also mentions some progress in other areas of Speech Processing (spea er recognition, speech synthesis, speech analysis and coding) using similar methodologies. It first gives a view of what the problems related to aulomatic speech processing are, and then describes the initial approaches that have been followed in order to address Chose problems . It then introduces thé methodological novelties that allowed for progress along three axes : from isolated-word recognition to continuous speech, from spea er-dependent recognition to spea er-independent, and from small vocabularies to large rocabularies. Special emphasis centers on tlie improvements made possible by Mar ov Models . and, more recently, hy Connectionist Models, resulting in progress simultaneously obtained along the above différent axes, in improved performance for difficult vocabularies, or in more robust systems . Some specialised hardware is also described, as well as the efforts aimed ai assessing Speech Recognition systems.Le but de cet article est de donner un aperçu des progrès récents obtenus dans le domaine de la reconnaissance automatique de la parole . Il traite essentiellement de la reconnaissance vocale, mais mentionne également les progrès réalisés dans d'autres domaines du Traitement Automatique de la Parole (Reconnaissance du Locuteur, Synthèse de Parole . Analyse et Codage), qui utilisent des méthodes voisines. Ensuite, sont introduites les nouveautés méthodologiques qui ont permis des progrès suivant trois axes : des mots isolés vers la parole continue, de la reconnaissance monolocuteur vers la reconnaissance multilocuteur, et des petits vocabulaires vers les grands vocabulaires . Une mention spéciale est accordée aux améliorations qui ont été rendues possibles par les Modèles Mar oviens, et, plus récemment, par les Modèles Connexionnistes . Ces méthodes ont conduit à des progrès obtenus concurremment suivant plusieurs axes, à des performances meilleures sur les vocabulaires difficiles, ou à des systèmes plus robustes . Quelques matériels spécialisés sont également décrits, ainsi que les efforts qui ont été consentis dans le but d'évaluer la qualité des systèmes de reconnaissanc