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A Wavelet Model for Vocalic Speech Coarticulation
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In: DTIC AND NTIS (1994)
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42 |
High-Performance Speech Recognition Using Consistency Modeling.
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In: DTIC AND NTIS (1994)
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43 |
Signal- and Listener-Based Factors in Complex Auditory Pattern Perception
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In: DTIC AND NTIS (1993)
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44 |
Application of a Back-Propagation Neural Network to Isolated-Word Speech Recognition
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In: DTIC AND NTIS (1993)
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45 |
The Use of Distinctive Features for Automatic Speech Recognition
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In: DTIC AND NTIS (1991)
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46 |
Massively Parallel Network Architectures for Automatic Recognition of Visual Speech Signals
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In: DTIC AND NTIS (1990)
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47 |
Acoustic Classification with Neural Networks
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In: DTIC AND NTIS (1990)
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48 |
Removal of Noise from Noise-Degraded Speech Signals. Panel on Removal of Noise from a Speech/Noise Signal
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In: DTIC AND NTIS (1989)
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49 |
Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition
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In: DTIC AND NTIS (1988)
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50 |
Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition.
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In: DTIC AND NTIS (1988)
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51 |
Speech Recognition: Acoustic-Phonetic Knowledge Acquisition and Representation.
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In: DTIC AND NTIS (1987)
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52 |
Time-Frequency Representations for Speech Signals.
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In: DTIC AND NTIS (1987)
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53 |
Speech Recognition: Acoustic, Phonetic and Lexical
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In: DTIC AND NTIS (1985)
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54 |
Speech Recognition: Acoustic, Phonetic and Lexical Knowledge
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In: DTIC AND NTIS (1985)
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55 |
Perception of Acoustic Transients.
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In: DTIC AND NTIS (1984)
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56 |
Processing Speech for Analysis Using Optical Fourier Techniques.
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In: DTIC AND NTIS (1984)
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57 |
Sensitivity Based Segmentation and Identification in Automatic Speech Recognition.
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In: DTIC AND NTIS (1984)
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Abstract:
This research program continued an investigation of sensitivity analysis, and its use in the segmentation and identification of the phonetic units of speech, that was initiated during the 1982 Summer Faculty Research Program. The elements of the sensitivity matrix, which express the relative change in each pole of the speech model to a relative change in each coefficient of the characteristic equation, were evaluated for an expanded set of data which consisted of six vowels contained in single words spoken in a simple carrier phrase by five males with differing dialects. The objectives were to evaluate the sensitivity matrix, interpret its changes during the production of the vowels, and to evaluate inter-speaker variations. It was determined that the sensitivity analysis (1) serves to segment the vowel interval, (2) provides a measure of when a vowel is on target, and (3) should provide sufficient information to identify each particular vowel. Based on the results presented, sensitivity analysis should result in more accurate segmentation and identification of phonemes and should provide a practicable framework for incorporation of acoustic-phonetic variance as well as time and talker normalization. (Author)
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Keyword:
*SPEECH RECOGNITION; ACOUSTIC SIGNALS; COEFFICIENTS; DIALECTS; IDENTIFICATION; MATHEMATICAL ANALYSIS; Numerical Mathematics; PHONEMES; PHONETICS; SEGMENTED; SENSITIVITY; SPEECH ANALYSIS; Voice Communications; VOWELS; WORDS(LANGUAGE)
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URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA142255 http://www.dtic.mil/docs/citations/ADA142255
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58 |
Auditory Induction of Discrete Tones in Signal Detection Tasks.
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In: DTIC AND NTIS (1983)
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59 |
Status Report on Speech Research, 1 April-30 June 1981.
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In: DTIC AND NTIS (1981)
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Symposium on the Recognition and Classification of Aural (Non-Speech) Signals Held at Cambridge, Massachusetts on 28-30 June 1978.
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In: DTIC AND NTIS (1980)
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