Application of continuous state Hidden Markov Models to a classical problem in speech recognition
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
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)).
|Journal||Computer Speech and Language|
|Early online date||14 May 2015|
|Publication status||Published - Mar 2016|
- Speech recognition, Hidden Markov Model, Recognition by synthesis