{"title":"Acoustic unit discovery and pronunciation generation from a grapheme-based lexicon","authors":"William Hartmann, A. Roy, L. Lamel, J. Gauvain","doi":"10.1109/ASRU.2013.6707760","DOIUrl":null,"url":null,"abstract":"We present a framework for discovering acoustic units and generating an associated pronunciation lexicon from an initial grapheme-based recognition system. Our approach consists of two distinct contributions. First, context-dependent grapheme models are clustered using a spectral clustering approach to create a set of phone-like acoustic units. Next, we transform the pronunciation lexicon using a statistical machine translation-based approach. Pronunciation hypotheses generated from a decoding of the training set are used to create a phrase-based translation table. We propose a novel method for scoring the phrase-based rules that significantly improves the output of the transformation process. Results on an English language dataset demonstrate the combined methods provide a 13% relative reduction in word error rate compared to a baseline grapheme-based system. Our approach could potentially be applied to low-resource languages without existing lexicons, such as in the Babel project.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASRU.2013.6707760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
We present a framework for discovering acoustic units and generating an associated pronunciation lexicon from an initial grapheme-based recognition system. Our approach consists of two distinct contributions. First, context-dependent grapheme models are clustered using a spectral clustering approach to create a set of phone-like acoustic units. Next, we transform the pronunciation lexicon using a statistical machine translation-based approach. Pronunciation hypotheses generated from a decoding of the training set are used to create a phrase-based translation table. We propose a novel method for scoring the phrase-based rules that significantly improves the output of the transformation process. Results on an English language dataset demonstrate the combined methods provide a 13% relative reduction in word error rate compared to a baseline grapheme-based system. Our approach could potentially be applied to low-resource languages without existing lexicons, such as in the Babel project.