Title
Consonant Recognition With Continuous-State Hidden Markov Models And Perceptually-Motivated Features
Abstract
Research into human perception of consonants has identified phoneme-specific perceptual cues. It has also been shown that the characteristics of the speech signal most useful for recognition depend on the specific speech sound. Typical ASR features and recognisers however neither vary with the type of sound nor relate directly to perceptual cues.We investigate classification and decoding of non-sonorant consonants using basic perceptually-motivated features - phoneme durations and energy in a few broad spectral bands. Our classification results using simple classifiers suggest that features optimal for human perception also perform best for machine classification. We show how characteristics of the models learned relate to knowledge of human speech perception.Recognition results using a continuous-state HMM (CSHMM) show accuracy similar to a discrete-state HMM with similar assumptions. We conclude by outlining how the CSHMM provides a mechanism to make use of other perceptually-important features by integration with similar models for recognition of voiced sounds.
Year
Venue
Keywords
2015
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5
Perceptual Features, CSHMM, Speech Analysis, Consonant Classification
Field
DocType
Citations 
Consonant,Maximum-entropy Markov model,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Decoding methods,Speech perception,Hidden Markov model,Spectral bands,Perception
Conference
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Weber, P.163.44
Colin J. Champion241.70
S. M. Houghton392.36
Peter Jancovic412021.46
Martin Russell533264.88