Out-of-vocabulary rate reduction through dispersion-based lexicon acquisition.
In this paper, we address the issue of the effective reduction of out-of-vocabulary (OOV) words for automatic speech recognition (ASR) systems. We first of all evaluate the OOV rates produced by different vocabulary sets selected from a corpus of British English according to the raw frequency of occ...
| Publicado en: | Literary & Linguistic Computing Vol. 15; no. 3; pp. 251 - 265 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
2000
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=80079472&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 80079472 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 02681145 BJ1 jtl: Literary & Linguistic Computing issn: 02681145 maglogo: N pubinfo: dt: 2000 vid: 15 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 80079472 10.1093/llc/15.3.251 ppf: 251 ppct: 14 formats: fmt: @attributes: type: P size: 600KB tig: atl: Out-of-vocabulary rate reduction through dispersion-based lexicon acquisition. aug: au: Fang, AC Huckvale, M affil: Department of Phonetics and Linguistics, University College London, Wolfson House, 4 Stephenson Way, London NW1 2HE, UK Corresponding author E-mail: alex|mark@phonetics.ucl.ac.uk su: Language acquisition Word frequency Automatic speech recognition Lexicon Word recognition Lexical access sug: subj: Language acquisition Word frequency Automatic speech recognition Lexicon Word recognition Lexical access ab: In this paper, we address the issue of the effective reduction of out-of-vocabulary (OOV) words for automatic speech recognition (ASR) systems. We first of all evaluate the OOV rates produced by different vocabulary sets selected from a corpus of British English according to the raw frequency of occurrence. We demonstrate that OOV rates in realistic input from unlimited domains are much higher than has been reported in the literature for ASR systems that typically deal with only a subset of the English language. To reduce OOV rates, we then propose that the textual dispersion of word types is a more effective selection criterion for the acquisition of lexicons than the conventional method of lexical selection according to raw frequencies of occurrence. We evaluate the performance of the adjusted frequency according to the index of dispersion, and the dispersion of word types among component text categories of the training corpus. With an 80,000-word vocabulary, the estimated frequency per million words adjusted according the index of dispersion achieves and improvement of 7.3 per cent over the frequency-based approach for a large set of testing material from a variety of sources. Vocabulary sets selected according to textual dispersion alone achieve a slightly better overall OOV reduction rate of 7.5 per cent. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Literary & Linguistic Computing holder: Oxford University Press / USA dt: @attributes: year: 2000 holdings: @attributes: islocal: N |
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