A closer look on hierarchical spectro-temporal features (HIST)

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2008, Pages 894-897

  Heckmann, Martin ^a   Domont, Xavier ^a,b   Joublin, Frank ^a   Goerick, Christian ^a  

^a HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Auditory; Non linear smoothing; Robust speech recognition; Spectro temporal

Indexed keywords

AUDITORY; AUDITORY CORTEX; DIGIT RECOGNITION; HIERARCHICAL LEVEL; LOCAL VARIATIONS; NON-LINEAR SMOOTHING; NON-NEGATIVE SPARSE CODING; PERFORMANCE ANALYSIS; RECEPTIVE FIELDS; ROBUST SPEECH RECOGNITION; SECOND LAYER; SPECTRO-TEMPORAL; SPECTROGRAMS; SPEECH FEATURES; TEMPORAL FEATURES; TIME AXIS;

FEATURE EXTRACTION; MAMMALS; SPEECH COMMUNICATION;

SPEECH RECOGNITION;

EID: 84867227177     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. H. Hermansky and N. Morgan, "RASTA processing of speech," IEEE Trans Speech and Audio Proc., vol. 2, no. 4, pp. 578-589, 1994.
2. S. Shamma, "On the role of space and time in auditory processing," Trends in Cognitive Sciences, vol. 5, no. 8, pp. 340-348, 2001.
3. M. Kleinschmidt, Robust Speech Recognition Based on Spectrotemporal Processing, Ph.D. thesis, Universität Oldenburg, 2002.
4. N. Mesgarani, M. Slaney, and SA Shamma, "Discrimination of speech from nonspeech based on multiscale spectro-temporal modulations," IEEE Trans. Audio, Speech and Language Proc., vol. 14, no. 3, pp. 920-930, 2006.
5. H. Wersing and E. Körner, "Learning Optimized Features for Hierarchical Models of Invariant Object Recognition," Neural Computation, vol. 15, no. 7, pp. 1559-1588, 2003.
6. X. Domont, M. Heckmann, F. Joublin, and C. Goerick, "Hierarchical sectro-temporal features for robust speech recognition," in Proc. IEEE Int. Conf. on Acoust., Speech, and Signal Proc. (ICASSP), Las Vegas, Nevada, 2008, pp. 4417-4420, IEEE.
7. M. Slaney, "An efficient implementation of the Patterson-Holdsworth auditory filterbank," Tech. Rep., Apple Computer Co., 1993, Technical report #35.
8. P.O. Hoyer, "Non-negative Matrix Factorization with Sparseness Constraints," The Journal of Machine Learning Research, vol. 5, pp. 1457-1469, 2004.
9. R. Leonard, "A Database for Speaker-independent Digit Recognition," in Proc. IEEE Int. Conf. on Acoust., Speech, and Signal Proc. (ICASSP), 1984, vol. 9.
10. D. Pearce and H.G. Hirsch, "The Aurora Experimental Framework for the Performance Evaluation of Speech Recognition Systems under Noisy Conditions," in Int. Conf. on Spoken Lang. Proc. 2000, ISCA.
11. G. Hirsch, "FaNTFiltering and Noise Adding Tool," Tech. Rep., Niederrhein University of Applied Sciences, http://dnt.-kr.hsnr.de/download. html.
12. A. Varga and H.J.M. Steeneken, "Assessment for automatic speech recognition II: NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems," Speech Communication, vol. 12, no. 3, pp. 247-251, 1993.
13. M. Heckmann, Adaptive Datenfusion für die Audio-visuelle Spracherkennung, Shaker, 2003.
14. C.P. Chen, K. Filali, and J.A. Bilmes, "Frontend Post-Processing and Backend Model Enhancement on the Aurora 2.0/3.0 Databases," in Int. Conf. on Spoken Lang. Proc. 2002, ISCA.