A probabilistic union model with automatic order selection for noisy speech recognition

Journal of the Acoustical Society of America

Volumn 110, Issue 3 I, 2001, Pages 1641-1648

  Jancovic P ^a   Ming, J ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; MARKOV PROCESSES; STATISTICAL METHODS;

HIDDEN MARKOV MODEL (HMM);

SPEECH RECOGNITION;

ACOUSTICS;

ALGORITHM; ARTICLE; NOISE; PRIORITY JOURNAL; PROBABILITY; SOUND DETECTION; SOUND INTENSITY; SPEECH DISCRIMINATION;

ALGORITHMS; HUMANS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NOISE; SPEECH PERCEPTION;

EID: 0034839117     PISSN: 00014966     EISSN: None     Source Type: Journal    
DOI: 10.1121/1.1387083     Document Type: Article

References (21)

1. Non-stationary multi-channel (multi-stream) processing towards robust and adaptive ASR
2. A new ASR approach based on independent processing and recombination of partial frequency bands
3. Sub-band based speech recognition
4. Robust parametric modeling of durations in hidden Markov models
5. A recombination model for multi-band speech recognition
6. Missing data techniques for robust speech recognition
7. Isolated-utterance speech recognition using hidden markov models with bounded state durations
8. Towards ASR on partially corrupted speech
9. A database for speaker-independent digit recognition
10. Using missing feature theory to actively select features for robust speech recognition with interruptions, filtering, and noise
11. Robust feature selection using probabilistic union models
12. Union: A new approach for combining sub-band observations for noisy speech recognition
13. A probabilistic union model for sub-band based noisy speech recognition
14. Union: A new approach for combining sub-band observations for noisy speech recognition
15. A probabilistic union model for partial and temporal corruption of speech
16. The full-combination sub-bands approach to noise robust HMM/ANN based ASR
17. Multi-band speech recognition in noisy environments
18. Experimental evaluation of duration modeling techniques for automatic speech recognition
19. Sub-band based recognition of noisy speech
20. Multi-band and adaptation approaches to robust speech recognition
21. State duration modeling in hidden Markov models