Journal ArticleDOI
In search of better pronunciation models for speech recognition
TLDR
A method for upgrading initially simple pronunciation models to new models that can explain several pronunciation variants of each word, and the introduction of such variants in a segment-based recognizer significantly improves the recognition accuracy.About:
This article is published in Speech Communication.The article was published on 1999-11-01. It has received 63 citations till now. The article focuses on the topics: Pronunciation & Word error rate.read more
Citations
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Journal ArticleDOI
Modeling pronunciation variation for ASR
Helmer Strik,Catia Cucchiarini +1 more
TL;DR: This contribution provides an overview of the publications on pronunciation variation modeling in automatic speech recognition, paying particular attention to the papers in this special issue and the papers presented at 'the Rolduc workshop'.
Patent
Method and apparatus for constructing and using syllable-like unit language models
TL;DR: In this paper, a method and computer-readable medium use syllable-like units (SLUs) to decode a pronunciation into a phonetic description, which are generally larger than a single phoneme but smaller than a word.
Journal ArticleDOI
Pronunciation modeling for ASR – knowledge-based and data-derived methods
TL;DR: A comparison between the knowledge-based and data-derived methods showed that 17% of variants generated by the phonological rules were also found using phone recognition, and this increases to 46% when the phone recognition output is smoothed by using D-trees.
PatentDOI
Method for adding phonetic descriptions to a speech recognition lexicon
TL;DR: In this paper, a method and computer-readable medium convert the text of a word and a user's pronunciation of the word into a phonetic description to be added to a speech recognition lexicon.
Journal ArticleDOI
A data-driven method for modeling pronunciation variation
TL;DR: This analysis shows that although modeling pronunciation variation brings about improvements, deteriorations are also introduced and it is not possible to improve ASR performance by excluding the rules that cause deteriorations, because these rules also produce a considerable number of improvements.
References
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Journal ArticleDOI
Speaker-independent phone recognition using hidden Markov models
Kai-Fu Lee,H.-W. Hon +1 more
TL;DR: The authors introduce the co-occurrence smoothing algorithm, which enables accurate recognition even with very limited training data, and can be used as benchmarks to evaluate future systems.
Journal ArticleDOI
An application of recurrent nets to phone probability estimation
TL;DR: Recognition results are presented for the DARPA TIMIT and Resource Management tasks, and it is concluded that recurrent nets are competitive with traditional means for performing phone probability estimation.
Proceedings ArticleDOI
A probabilistic framework for feature-based speech recognition
TL;DR: This paper examines a maximum a-posteriori decoding strategy for feature-based recognizers and develops a normalization criterion that is useful for a segment-based Viterbi or A* search.
Proceedings Article
High performance speaker-independent phone recognition using CDHMM.
Lori Lamel,Jean-Luc Gauvain +1 more
TL;DR: It is shown that it is worthwhile to perform phone recognition experiments as opposed to only focusing attention on word recognition results, and high phone accuracies on three corpora: WSJ0, BREF and TIMIT are reported.