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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 Page: 1 2 3 4 5 Hits 41 – 60 of 89 41 A Wavelet Model for Vocalic Speech Coarticulation Lange, R. C. In: DTIC AND NTIS (1994) BASE Show details 42 High-Performance Speech Recognition Using Consistency Modeling. Digalakis, Vassilios; Murveit, Hy; Monaco, Peter... In: DTIC AND NTIS (1994) BASE Show details 43 Signal- and Listener-Based Factors in Complex Auditory Pattern Perception Samuel, Arthur G. In: DTIC AND NTIS (1993) BASE Show details 44 Application of a Back-Propagation Neural Network to Isolated-Word Speech Recognition Le, Chau G. In: DTIC AND NTIS (1993) BASE Show details 45 The Use of Distinctive Features for Automatic Speech Recognition Meng, Helen M. In: DTIC AND NTIS (1991) BASE Show details 46 Massively Parallel Network Architectures for Automatic Recognition of Visual Speech Signals Sejnowski, Terrence J.; Goldstein, Moise In: DTIC AND NTIS (1990) BASE Show details 47 Acoustic Classification with Neural Networks Weaver, Charles S.; Branch, Kristina D.; Rumelhart, David E.; Ostrem, John S. In: DTIC AND NTIS (1990) Abstract: A major objective of the research that is described in this report was the preliminary development and testing of neural networks to aid a sonar operator in detecting and classifying sonar signals and signatures. Two networks were developed to recognize a set of sounds that is representative of sonar pulse type signals. The pattern vector inputs were derived from the signal spectra. Both networks are time delay neural networks that classify by recognizing spectral shapes and the first and second differences of spectral shapes. One is a back propagation network and the other is a network that does not contain weights and thus is much simpler than back propagation networks to implement in hardware. Both networks demonstrated a very low false alarm rate and were as accurate or significantly more accurate than the test subjects at signal detection and classification. When the subjects were prompted by a network they were more accurate at finding and classifying the more difficult to detect signals. Our preliminary conclusion is that neural networks will have great value in detecting sonar pulses which is an important signal class. The architecture and understanding of the weightless network that was developed during Phase I leads us to believe that networks of this type may become important in sonar signal analysis. (jhd) Keyword: *NEURAL NETS; *SONAR SOUND ANALYZERS; *TARGET CLASSIFICATION; ACCURACY; ACOUSTIC DETECTION; Acoustic Detection and Detectors; ACOUSTIC SIGNATURES; Computer Programming and Software; DELAY; PE61153N; PROPAGATION; SHAPE; SONAR PERSONNEL; SONAR PULSES; SONAR SIGNALS; TIME INTERVALS; WEIGHTLESSNESS URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA219464 http://www.dtic.mil/docs/citations/ADA219464 BASE Hide details 48 Removal of Noise from Noise-Degraded Speech Signals. Panel on Removal of Noise from a Speech/Noise Signal In: DTIC AND NTIS (1989) BASE Show details 49 Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition Glass, James R. In: DTIC AND NTIS (1988) BASE Show details 50 Finding Acoustic Regularities in Speech: Applications to Phonetic Recognition. Glass, James R. In: DTIC AND NTIS (1988) BASE Show details 51 Speech Recognition: Acoustic-Phonetic Knowledge Acquisition and Representation. Zue, Victor W In: DTIC AND NTIS (1987) BASE Show details 52 Time-Frequency Representations for Speech Signals. Riley, Michael D In: DTIC AND NTIS (1987) BASE Show details 53 Speech Recognition: Acoustic, Phonetic and Lexical Zue, V W In: DTIC AND NTIS (1985) BASE Show details 54 Speech Recognition: Acoustic, Phonetic and Lexical Knowledge Zue, V W In: DTIC AND NTIS (1985) BASE Show details 55 Perception of Acoustic Transients. Howard,J H , Jr In: DTIC AND NTIS (1984) BASE Show details 56 Processing Speech for Analysis Using Optical Fourier Techniques. Jones,D L In: DTIC AND NTIS (1984) BASE Show details 57 Sensitivity Based Segmentation and Identification in Automatic Speech Recognition. Absher,R In: DTIC AND NTIS (1984) BASE Show details 58 Auditory Induction of Discrete Tones in Signal Detection Tasks. Bennett,K B; Parasuraman,R; Howard,J H , Jr... In: DTIC AND NTIS (1983) BASE Show details 59 Status Report on Speech Research, 1 April-30 June 1981. In: DTIC AND NTIS (1981) BASE Show details 60 Symposium on the Recognition and Classification of Aural (Non-Speech) Signals Held at Cambridge, Massachusetts on 28-30 June 1978. Getty,David J; Howard,James H , Jr In: DTIC AND NTIS (1980) BASE Show details Page: 1 2 3 4 5

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