Lexicon-building methods for an acoustic sub-word based speech recognizer
03 Apr 1990-Vol. 1990, pp 729-732
TL;DR: The use of an acoustic subword unit (ASWU)-based speech recognition system for the recognition of isolated words is discussed and it is shown that the use of a modified k-means algorithm on the likelihoods derived through the Viterbi algorithm provides the best deterministic-type of word lexicon.
Abstract: The use of an acoustic subword unit (ASWU)-based speech recognition system for the recognition of isolated words is discussed. Some methods are proposed for generating the deterministic and the statistical types of word lexicon. It is shown that the use of a modified k-means algorithm on the likelihoods derived through the Viterbi algorithm provides the best deterministic-type of word lexicon. However, the ASWU-based speech recognizer leads to better performance with the statistical type of word lexicon than with the deterministic type. Improving the design of the word lexicon makes it possible to narrow the gap in the recognition performances of the whole word unit (WWU)-based and the ASWU-based speech recognizers considerably. Further improvements are expected by designing the word lexicon better. >
Citations
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01 Jan 1999
TL;DR: Problems with the phoneme as the basic subword unit in speech recognition are raised, suggesting that finer-grained control is needed to capture the sort of pronunciation variability observed in spontaneous speech.
Abstract: The notion that a word is composed of a sequence of phone segments, sometimes referred to as ‘beads on a string’, has formed the basis of most speech recognition work for over 15 years. However, as more researchers tackle spontaneous speech recognition tasks, that view is being called into question. This paper raises problems with the phoneme as the basic subword unit in speech recognition, suggesting that finer-grained control is needed to capture the sort of pronunciation variability observed in spontaneous speech. We offer two different alternatives – automatically derived subword units and linguistically motivated distinctive feature systems – and discuss current work in these directions. In addition, we look at problems that arise in acoustic modeling when trying to incorporate higher-level structure with these two strategies.
151 citations
Cites background from "Lexicon-building methods for an aco..."
...ASWUs were proposed several years ago [10, 11, 12, 13], but they faded from view as speaker-independent recognition became the primary goal, because of the difficulty of distinguishing speaker variability from real pronunciation differences....
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IBM1
TL;DR: A method for combining phonetic and fenonic models is presented and results of experiments with speaker-dependent and speaker-independent models on several isolated-word recognition tasks are reported.
Abstract: A technique for constructing Markov models for the acoustic representation of words is described. Word models are constructed from models of subword units called fenones. Fenones represent very short speech events and are obtained automatically through the use of a vector quantizer. The fenonic baseform for a word-i.e., the sequence of fenones used to represent the word-is derived automatically from one or more utterances of that word. Since the word models are all composed from a small inventory of subword models, training for large-vocabulary speech recognition systems can be accomplished with a small training script. A method for combining phonetic and fenonic models is presented. Results of experiments with speaker-dependent and speaker-independent models on several isolated-word recognition tasks are reported. The results are compared with those for phonetics-based Markov models and template-based dynamic programming (DP) matching. >
67 citations
TL;DR: A joint solution to the related problems of learning a unit inventory and corresponding lexicon from data on a speaker-independent read speech task with a 1k vocabulary, the proposed algorithm outperforms phone-based systems at both high and low complexities.
Abstract: Although most parameters in a speech recognition system are estimated from data by the use of an objective function, the unit inventory and lexicon are generally hand crafted and therefore unlikely to be optimal. This paper proposes a joint solution to the related problems of learning a unit inventory and corresponding lexicon from data. On a speaker-independent read speech task with a 1k vocabulary, the proposed algorithm outperforms phone-based systems at both high and low complexities. Obwohl die meisten Parameter eines Spracherkennungssystems aus Daten geschatzt werden, ist die Wahl der akustischen Grundeinheiten und des Lexikons normalerweise nicht automatisch und deshalb wahrscheinlich nicht optimal. Dieser Artikel stellt einen kombinierten Ansatz fur die Losung dieser verwandten Probleme dar - das Lernen von akustischen Grundeinheiten und des zugehorigen Lexikons aus Daten. Experimente mit sprecher-unabhangigen gelesenen Sprachdaten mit einem Vokabular von 1000 Wortern zeigen, da?s der vorgestellte Ansatz besser ist als ein System niedriger oder hoherer Komplexitat, das auf Phonemen basiert ist. Bien que la plupart des parametres dans un systeme de reconnaissance de la parole soient estimes a partie des donnees en utilisant une fonction objective, l'inventaire des unites acoustiques et le lexique sont generalement crees a la main, et donc susceptibles de ne pas etre optimeux. Cette etude propose une solution conjointe aux problemes interdependants que sont l'apprentissage a partir des donnees d'un inventaire des unites acoustiques et du lexique correspondant. Nous avons teste l'algorithme propose sur des echantillons lus, en reconnaissance independantes du locuteur avec un vocabulaire de 1k: il surpasse les systemes phonetiques en faible ou forte complexite.
66 citations
Cites background or methods from "Lexicon-building methods for an aco..."
...Taking an approach similar to that in (Svendsen and Soong, 1987; Paliwal, 1990), the maximum likelihood segmentation of the training data are found by the use of dynamic programming....
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...…therefore directly represent unit models and clustering addresses both the inventory and model design problems, whereas in (Svendsen et al., 1989; Paliwal, 1990; Holter and Svendsen, 1997a) unit model parameters had to be estimated in a separate step from the data partition de®ned by clustering....
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...Cluster centroids therefore directly represent unit models and clustering addresses both the inventory and model design problems, whereas in (Svendsen et al., 1989; Paliwal, 1990; Holter and Svendsen, 1997a) unit model parameters had to be estimated in a separate step from the data partition de®ned by clustering....
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...The clustering algorithm used here diers from that used in (Svendsen et al., 1989; Paliwal, 1990; Holter and Svendsen, 1997a) in that maximum likelihood is used as an objective rather than minimum Euclidean distance....
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...The related problem of de®ning a lexicon in terms of these ASWUs has also received attention (e.g., Paliwal, 1990; Svendsen et al., 1995)....
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TL;DR: A maximum likelihood based algorithm for fully automatic data-driven modelling of pronunciation, given a set of subword hidden Markov models (HMMs) and acoustic tokens of a word to create a consistent framework for optimisation of automatic speech recognition systems.
Abstract: This paper addresses the problem of generating lexical word representations that properly represent natural pronunciation variations for the purpose of improved speech recognition accuracy. In order to create a consistent framework for optimisation of automatic speech recognition systems, we present a maximum likelihood based algorithm for fully automatic data-driven modelling of pronunciation, given a set of subword hidden Markov models (HMMs) and acoustic tokens of a word. We also propose an extension of this formulation in order to achieve optimal modelling of pronunciation variations. Since different words will not in general exhibit the same amount of pronunciation variation, the procedure allows words to be represented by a different number of baseforms. The methods improve the subword description of the vocabulary words and have been shown to improve recognition performance on the DARPA Resource Management task. Dieser Beitrag behandelt das Problem der Erzeugung lexikalischer Wortdarstellungen, die die naturliche Variation der Aussprache von Wortern geeignet reprasentieren, um auf diese Weise die Genauigkeit eines Spracherkennungssystemes zu erhohen. Um einen einheitlichen Ansatz fur die Optimierung von Spracherkennungssystemen zu entwickeln, wird ein maximum-likelihood-basiertes Verfahren zur vollautomatischen datengesteuerten Modellierung der Aussprache von Wortern vorgestellt. Dieses Verfahren basiert auf einen Satz von Teilwortern mit verborgenen Markov-Modellen und akustische Proben eines Wortes. Au?serdem wird eine Erweiterung dieses Verfahrens vorgeschlagen, um eine optimale Modellierung der Aussprachevariation zu erzielen. Da unterschiedliche Worter im allgemeinen nicht den gleichen Grad der Aussprachevariation aufweisen, erlaubt das vorgestellte Verfahren, Worter durch eine unterschiedliche Anzahl von Basisformen darzustellen. Diese Verfahren verbessern die Teilwort-Darstellung der Worter im Wortschatz, und es konnte gezeigt werden, da?s damit die Erkennungsleistung fur den DARPA Resource Management Task verbessert wird. Cette communication aborde le probleme de la generation des representations des mots lexicaux representant des variations naturelles de la prononciation. Le but est d'ameliorer la precision en ce qui concerne la reconnaissance de la parole. Afin de creer un cadre consistant pour l'optimisation des systemes automatiques pour la reconnaissance de la parole, on presente ici un algorithme base sur la classification au maximum de vraisemblance pour la modelisation automatique de la prononciation. Cette modelisation utilise une rame d'unites de parole aux modeles Markov dissimules et des echantillons acoustiques d'un mot. On propose aussi une extension de cette formulation afin d'obtenir une modelisation optimale des variations de la prononciation. Puisque de differents mots n'exposent pas, en general, le meme degre de variation de la prononciation, cette methode permet une representation des mots par un nombre varie d'entrees lexicales. La methode ameliore la description d'unites de parole des mots du vocabulaire, chose qui a demontre une amelioration de la performance de la reconnaissance en ce qui concerne la tâche de la DARPA Resource Management.
63 citations
Proceedings Article•
01 Oct 2013TL;DR: An unsupervised alternative ‐ requiring no language-specific knowledge ‐ to the conventional manual approach for creating pronunciation dictionaries is proposed, which jointly discovers the phonetic inventory and the Letter-to-Sound mapping rules in a language using only transcribed data.
Abstract: The creation of a pronunciation lexicon remains the most inefficient process in developing an Automatic Speech Recognizer (ASR). In this paper, we propose an unsupervised alternative ‐ requiring no language-specific knowledge ‐ to the conventional manual approach for creating pronunciation dictionaries. We present a hierarchical Bayesian model, which jointly discovers the phonetic inventory and the Letter-to-Sound (L2S) mapping rules in a language using only transcribed data. When tested on a corpus of spontaneous queries, the results demonstrate the superiority of the proposed joint learning scheme over its sequential counterpart, in which the latent phonetic inventory and L2S mappings are learned separately. Furthermore, the recognizers built with the automatically induced lexicon consistently outperform grapheme-based recognizers and even approach the performance of recognition systems trained using conventional supervised procedures.
41 citations
Additional excerpts
...Various algorithms for learning sub-word based pronunciations were proposed in (Lee et al., 1988; Fukada et al., 1996; Bacchiani and Ostendorf, 1999; Paliwal, 1990)....
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References
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11 Apr 1988
TL;DR: Several speaker dependent automatic speech recognition (ASR) experiments were performed to assess the effectiveness of the training set generated using the new procedure, which showed that the string accuracy was about 98% when tested on independent sets of test sentences for the three talkers.
Abstract: Past research has shown that a connected digit recognition system, based on either word templates or word hidden Markov models (HMM), could effectively be trained using a segmental k-means training procedure. In these studies, a set of randomly generated digit strings of variable length was used to train the recognizer. However, problems were encountered when this training procedure was extended to systems with medium to large vocabularies. For a training set to be effective, it should represent each vocabulary word and its acoustic variability within the context of all valid input strings defined by a task dependent grammar. A procedure to generate a training set of sentences with these properties is proposed. Using this procedure, a training set of sentences was generated for a connected word recognition system simulating an airline flight reservation task. Several speaker dependent automatic speech recognition (ASR) experiments were performed to assess the effectiveness of the training set generated using the new procedure. The results of these experiments showed that the string accuracy was about 98% when tested on independent sets of test sentences for the three talkers. >
3 citations