EUROSPEECH 2001 Scandinavia
This paper addresses the problem of classification of speech transition sounds. A number of non parametric classifiers are compared, and it is shown that some non-parametric classifiers have considerable advantages over traditional hidden Markov models. Among the non parametric classifiers, support vector machines were found the most suitable and the easiest to tune. Some of the reasons for the superiority of non parametric classifiers will be discussed. The algorithm was tested on the voiced stop consonant phones extracted from the TIMIT corpus and resulted in very low error rates.
Bibliographic reference. Litichever, Zeev / Chazan, Dan (2001): "Classification of transition sounds with application to automatic speech recognition", In EUROSPEECH-2001, 1789-1792.