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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) BASE Show 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) Abstract: The study reported in this paper addresses three issues related to phonetic classification: 1) whether it is important to choose an appropriate signal representation, 2) whether there are any advantages in extracting acoustic attributes over directly using the spectral information, and 3) whether it is advantageous to introduce an intermediate set of linguistic units, i.e. distinctive features. To restrict the scope of our study, we focused on 16 vowels in American English, and investigated classification performance using an artificial neural network with nearly 22,000 vowels tokens from 550 speakers excised from the TIMIT corpus. Our results indicate that 1) the combined outputs of Seneff's auditory model outperforms five other representations with both undegraded and noisy speech, 2) acoustic attributes give similar performance to raw spectral information, but at potentially considerable computational savings, and 3) the distinctive feature representation gives similar performance to direct vowel classification, but potentially offers a more flexible mechanism for describing context dependency. ; Supported in part by DARPA. Keyword: *INFORMATION RETRIEVAL; *PHONETICS; CLASSIFICATION; COMPUTATIONS; ENGLISH LANGUAGE; HEARING; Information Science; Linguistics; MODELS; NEURAL NETS; SIGNALS; SPECTRA; SPEECH RECOGNITION; Voice Communications; VOWELS URL: http://www.dtic.mil/docs/citations/ADA458588 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA458588 BASE Hide details

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