{"title":"Very large vocabulary isolated utterance recognition: a comparison between one pass and two pass strategies","authors":"L. Fissore, P. Laface, G. Micca, R. Pieraccini","doi":"10.1109/ICASSP.1988.196549","DOIUrl":null,"url":null,"abstract":"A system for recognizing isolated utterances belonging to a very large vocabulary is presented that follows a two-pass strategy. The first step, hypothesization, consists in the selection of a subset of word candidates, starting from the segmentation of speech into six broad phonetic classes. This module is implemented through a dynamic programming algorithm working in a three-dimensional space. The search is performed on a tree representing a coarse description of the lexicon. The second step is the search for the best N candidates according to a maximum-likelihood criterion. Each word candidate is represented by a graph of subword hidden Markov models, and a tree structure of the whole word subset is built on line for an efficient implementation of the Viterbi algorithm. A comparison with a direct approach that does not use the hypothesization module shows that the two-pass approach has the same performance with an 80% reduction in computational complexity.<<ETX>>","PeriodicalId":448544,"journal":{"name":"ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1988.196549","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
A system for recognizing isolated utterances belonging to a very large vocabulary is presented that follows a two-pass strategy. The first step, hypothesization, consists in the selection of a subset of word candidates, starting from the segmentation of speech into six broad phonetic classes. This module is implemented through a dynamic programming algorithm working in a three-dimensional space. The search is performed on a tree representing a coarse description of the lexicon. The second step is the search for the best N candidates according to a maximum-likelihood criterion. Each word candidate is represented by a graph of subword hidden Markov models, and a tree structure of the whole word subset is built on line for an efficient implementation of the Viterbi algorithm. A comparison with a direct approach that does not use the hypothesization module shows that the two-pass approach has the same performance with an 80% reduction in computational complexity.<>