Application of continuous state Hidden Markov Models to a classical problem in speech recognition. (March 2016)

• Record Type: Journal Article
• Authors: Champion, Colin; Houghton, S.M.
• Is Part Of: Computer speech & language. Volume 36(2016)
• Page Start: 347
• Page End: 364
• Publication Date: 2016-03
• Subjects: Speech recognition -- Hidden Markov Model -- Recognition by synthesis; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2015.05.001
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract : Highlights: We outline a model in which dynamic properties are explicit components. These dynamic properties reflect the smooth motion of speech articulators. We construct an optimal algorithm for decoding a signal satisfying the model. The underlying principle is that of the continuous state Hidden Markov Model. We show that the algorithm achieves its aims in an experiment on toy data. Abstract: This paper describes an optimal algorithm using continuous state Hidden Markov Models for solving the HMS decoding problem, which is the problem of recovering an underlying sequence of phonetic units from measurements of smoothly varying acoustic features, thus inverting the speech generation process described by Holmes, Mattingly and Shearme in a well known paper (Speech synthesis by rule. Lang. Speech 7 (1964)).