Lexical Access for Speech Understanding using Minimum Message Length Encoding
The Lexical Access Problem consists of determining the intended sequence of words corresponding to an input sequence of phonemes (basic speech sounds) that come from a low-level phoneme recognizer. In this paper we present an information-theoretic approach based on the Minimum Message Length Criteri...
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creator | Thomas, Ian Zukerman, Ingrid Oliver, Jonathan Albrecht, David Raskutti, Bhavani |
description | The Lexical Access Problem consists of determining the intended sequence of
words corresponding to an input sequence of phonemes (basic speech sounds) that
come from a low-level phoneme recognizer. In this paper we present an
information-theoretic approach based on the Minimum Message Length Criterion
for solving the Lexical Access Problem. We model sentences using phoneme
realizations seen in training, and word and part-of-speech information obtained
from text corpora. We show results on multiple-speaker, continuous, read speech
and discuss a heuristic using equivalence classes of similar sounding words
which speeds up the recognition process without significant deterioration in
recognition accuracy. |
doi_str_mv | 10.48550/arxiv.1302.1572 |
format | Article |
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words corresponding to an input sequence of phonemes (basic speech sounds) that
come from a low-level phoneme recognizer. In this paper we present an
information-theoretic approach based on the Minimum Message Length Criterion
for solving the Lexical Access Problem. We model sentences using phoneme
realizations seen in training, and word and part-of-speech information obtained
from text corpora. We show results on multiple-speaker, continuous, read speech
and discuss a heuristic using equivalence classes of similar sounding words
which speeds up the recognition process without significant deterioration in
recognition accuracy.</description><identifier>DOI: 10.48550/arxiv.1302.1572</identifier><language>eng</language><subject>Computer Science - Computation and Language</subject><creationdate>2013-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1302.1572$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1302.1572$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Thomas, Ian</creatorcontrib><creatorcontrib>Zukerman, Ingrid</creatorcontrib><creatorcontrib>Oliver, Jonathan</creatorcontrib><creatorcontrib>Albrecht, David</creatorcontrib><creatorcontrib>Raskutti, Bhavani</creatorcontrib><title>Lexical Access for Speech Understanding using Minimum Message Length Encoding</title><description>The Lexical Access Problem consists of determining the intended sequence of
words corresponding to an input sequence of phonemes (basic speech sounds) that
come from a low-level phoneme recognizer. In this paper we present an
information-theoretic approach based on the Minimum Message Length Criterion
for solving the Lexical Access Problem. We model sentences using phoneme
realizations seen in training, and word and part-of-speech information obtained
from text corpora. We show results on multiple-speaker, continuous, read speech
and discuss a heuristic using equivalence classes of similar sounding words
which speeds up the recognition process without significant deterioration in
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words corresponding to an input sequence of phonemes (basic speech sounds) that
come from a low-level phoneme recognizer. In this paper we present an
information-theoretic approach based on the Minimum Message Length Criterion
for solving the Lexical Access Problem. We model sentences using phoneme
realizations seen in training, and word and part-of-speech information obtained
from text corpora. We show results on multiple-speaker, continuous, read speech
and discuss a heuristic using equivalence classes of similar sounding words
which speeds up the recognition process without significant deterioration in
recognition accuracy.</abstract><doi>10.48550/arxiv.1302.1572</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computation and Language |
title | Lexical Access for Speech Understanding using Minimum Message Length Encoding |
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