A characterization of the problem of new, out-of-vocabulary words in continuous-speech recognition and understanding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 167-173).by Irvine Lee Hetherington.Ph.D

Linguistically-motivated sub-word modeling with applications to speech recognition

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 173-185).Despite the proliferation of speech-enabled applications and devices, speech-driven human-machine interaction still faces several challenges. One of theses issues is the new word or the out-of-vocabulary (OOV) problem, which occurs when the underlying automatic speech recognizer (ASR) encounters a word it does not "know". With ASR being deployed in constantly evolving domains such as restaurant ratings, or music querying, as well as on handheld devices, the new word problem continues to arise.This thesis is concerned with the OOV problem, and in particular with the process of modeling and learning the lexical properties of an OOV word through a linguistically-motivated sub-syllabic model. The linguistic model is designed using a context-free grammar which describes the sub-syllabic structure of English words, and encapsulates phonotactic and phonological constraints. The context-free grammar is supported by a probability model, which captures the statistics of the parses generated by the grammar and encodes spatio-temporal context. The two main outcomes of the grammar design are: (1) sub-word units, which encode pronunciation information, and can be viewed as clusters of phonemes; and (2) a high-quality alignment between graphemic and sub-word units, which results in hybrid entities denoted as spellnemes. The spellneme units are used in the design of a statistical bi-directional letter-to-sound (L2S) model, which plays a significant role in automatically learning the spelling and pronunciation of a new word.The sub-word units and the L2S model are assessed on the task of automatic lexicon generation. In a first set of experiments, knowledge of the spelling of the lexicon is assumed. It is shown that the phonemic pronunciations associated with the lexicon can be successfully learned using the L2S model as well as a sub-word recognizer.(cont.) In a second set of experiments, the assumption of perfect spelling knowledge is relaxed, and an iterative and unsupervised algorithm, denoted as Turbo-style, makes use of spoken instances of both spellings and words to learn the lexical entries in a dictionary.Sub-word speech recognition is also embedded in a parallel fashion as a backoff mechanism for a word recognizer. The resulting hybrid model is evaluated in a lexical access application, whereby a word recognizer first attempts to recognize an isolated word. Upon failure of the word recognizer, the sub-word recognizer is manually triggered. Preliminary results show that such a hybrid set-up outperforms a large-vocabulary recognizer.Finally, the sub-word units are embedded in a flat hybrid OOV model for continuous ASR. The hybrid ASR is deployed as a front-end to a song retrieval application, which is queried via spoken lyrics. Vocabulary compression and open-ended query recognition are achieved by designing a hybrid ASR. The performance of the frontend recognition system is reported in terms of sentence, word, and sub-word error rates. The hybrid ASR is shown to outperform a word-only system over a range of out-of-vocabulary rates (1%-50%). The retrieval performance is thoroughly assessed as a fmnction of ASR N-best size, language model order, and the index size. Moreover, it is shown that the sub-words outperform alternative linguistically-motivated sub-lexical units such as phonemes. Finally, it is observed that a dramatic vocabulary compression - by more than a factor of 10 - is accompanied by a minor loss in song retrieval performance.by Ghinwa F. Choueiter.Ph.D

Modèles de langage ad hoc pour la reconnaissance automatique de la parole

Les trois piliers d un système de reconnaissance automatique de la parole sont le lexique,le modèle de langage et le modèle acoustique. Le lexique fournit l ensemble des mots qu il est possible de transcrire, associés à leur prononciation. Le modèle acoustique donne une indication sur la manière dont sont réalisés les unités acoustiques et le modèle de langage apporte la connaissance de la manière dont les mots s enchaînent.Dans les systèmes de reconnaissance automatique de la parole markoviens, les modèles acoustiques et linguistiques sont de nature statistique. Leur estimation nécessite de gros volumes de données sélectionnées, normalisées et annotées.A l heure actuelle, les données disponibles sur le Web constituent de loin le plus gros corpus textuel disponible pour les langues française et anglaise. Ces données peuvent potentiellement servir à la construction du lexique et à l estimation et l adaptation du modèle de langage. Le travail présenté ici consiste à proposer de nouvelles approches permettant de tirer parti de cette ressource.Ce document est organisé en deux parties. La première traite de l utilisation des données présentes sur le Web pour mettre à jour dynamiquement le lexique du moteur de reconnaissance automatique de la parole. L approche proposée consiste à augmenter dynamiquement et localement le lexique du moteur de reconnaissance automatique de la parole lorsque des mots inconnus apparaissent dans le flux de parole. Les nouveaux mots sont extraits du Web grâce à la formulation automatique de requêtes soumises à un moteur de recherche. La phonétisation de ces mots est obtenue grâce à un phonétiseur automatique.La seconde partie présente une nouvelle manière de considérer l information que représente le Web et des éléments de la théorie des possibilités sont utilisés pour la modéliser. Un modèle de langage possibiliste est alors proposé. Il fournit une estimation de la possibilité d une séquence de mots à partir de connaissances relatives à existence de séquences de mots sur le Web. Un modèle probabiliste Web reposant sur le compte de documents fourni par un moteur de recherche Web est également présenté. Plusieurs approches permettant de combiner ces modèles avec des modèles probabilistes classiques estimés sur corpus sont proposées. Les résultats montrent que combiner les modèles probabilistes et possibilistes donne de meilleurs résultats que es modèles probabilistes classiques. De plus, les modèles estimés à partir des données Web donnent de meilleurs résultats que ceux estimés sur corpus.The three pillars of an automatic speech recognition system are the lexicon, the languagemodel and the acoustic model. The lexicon provides all the words that can betranscribed, associated with their pronunciation. The acoustic model provides an indicationof how the phone units are pronounced, and the language model brings theknowledge of how words are linked. In modern automatic speech recognition systems,the acoustic and language models are statistical. Their estimation requires large volumesof data selected, standardized and annotated.At present, the Web is by far the largest textual corpus available for English andFrench languages. The data it holds can potentially be used to build the vocabularyand the estimation and adaptation of language model. The work presented here is topropose new approaches to take advantage of this resource in the context of languagemodeling.The document is organized into two parts. The first deals with the use of the Webdata to dynamically update the lexicon of the automatic speech recognition system.The proposed approach consists on increasing dynamically and locally the lexicon onlywhen unknown words appear in the speech. New words are extracted from the Webthrough the formulation of queries submitted toWeb search engines. The phonetizationof the words is obtained by an automatic grapheme-to-phoneme transcriber.The second part of the document presents a new way of handling the informationcontained on the Web by relying on possibility theory concepts. A Web-based possibilisticlanguage model is proposed. It provides an estition of the possibility of a wordsequence from knowledge of the existence of its sub-sequences on the Web. A probabilisticWeb-based language model is also proposed. It relies on Web document countsto estimate n-gram probabilities. Several approaches for combining these models withclassical models are proposed. The results show that combining probabilistic and possibilisticmodels gives better results than classical probabilistic models alone. In addition,the models estimated from Web data perform better than those estimated on corpus.AVIGNON-Bib. numérique (840079901) / SudocSudocFranceF