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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Mandarin-English Information (MEI): investigating translingual speech retrieval Meng, Helen M.; Chen, Berlin; Khudanpur, Sanjeev... In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 18 (2004) 2, 163-180 OLC Linguistik Show details 2 Multi-Scale Spoken Document Retrieval for Cantonese Broadcast News Lo, Wai-Kit; Meng, Helen M.; Ching, P.C. In: International journal of speech technology. - Boston, Mass. [u.a.] : Kluwer Acad. Publ. 7 (2004) 2-3, 203-220 OLC Linguistik Show details 3 Initial Development Towards a Trilingual Speech Interface for Financial Information Inquiries Meng, Helen M. In: International journal of speech technology. - Boston, Mass. [u.a.] : Kluwer Acad. Publ. 3 (1999) 2, 83-92 OLC Linguistik Show details 4 Phonological parsing for bi-directional letter-to-sound/sound-to-letter generation Meng, Helen M. - : Massachusetts Institute of Technology, 1995 BASE Show details 5 Phonological Parsing for Bi-directional Letter-to-Sound/Sound-to-Letter Generation Meng, Helen M.; Seneff, Stephanie; Zue, Victor W. In: DTIC (1994) Abstract: In this paper, we describe a reversible letter-to-sound/sound to-letter generation system based on an approach which combines a rule-based formalism with data-driven techniques. We adopt a probabilistic parsing strategy to provide a hierarchical lexical analysis of a word, including information such as morphology, stress, syllabification, phonemics and graphemics. Long-distance constraints are propagated by enforcing local constraints throughout the hierarchy. Our training and testing corpora are derived from the high-frequency portion of the Brown Corpus (10,000 words), augmented with markers indicating stress and word morphology. We evaluated our performance based on an unseen test set. The percentage of nonparsable words for letter-to-sound and sound-to-letter generation were 6% and 5% respectively. Of the remaining words our system achieved a word accuracy of 71.8% and a phoneme accuracy of 92.5% for letter-to-sound generation, and a word accuracy of 55.8% and letter accuracy of 89.4% for sound-to-letter generation. We also compared our hierarchical approach with an alternative, single-layer approach to demonstrate how the hierarchy provides a parsimonious description for English orthographic-phonological regularities, while simultaneously attaining competitive generation accuracy. ; Supported in part by DARPA. Keyword: *PARSERS; *WORDS(LANGUAGE); ACCURACY; COMPETITION; HIERARCHIES; INDICATORS; LEXICOGRAPHY; Linguistics; MARKERS; MORPHOLOGY; PHONETICS; PROBABILITY; STRATEGY; STRESSES; TEST SETS URL: http://www.dtic.mil/docs/citations/ADA458591 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458591 BASE Hide details 6 The Use of Distinctive Features for Automatic Speech Recognition Meng, Helen M. In: DTIC AND NTIS (1991) BASE Show details 7 Signal Representation, Attribute Extraction and, the Use of Distinctive Features for Phonetic Classification Meng, Helen M.; Zue, Victor W.; Leung, Hong C. In: DTIC (1991) BASE Show details

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