Title :
Parametric trajectory models for speech recognition
Author :
Gish, H. ; Ng, Kenney
Author_Institution :
BBN Syst. & Technol. Corp., Cambridge, MA, USA
Abstract :
The basic motivation for employing trajectory models for speech recognition is that sequences of speech features are statistically dependent and that the effective and efficient modeling of the speech process will incorporate this dependency. In our previous work we presented an approach to modeling the speech process with trajectories. In this paper we continue our development of parametric trajectory models for speech recognition. We extend our models to include time-varying covariances and describe our approach for defining a metric between speech segments based on trajectory models; it is important in developing mixture models of trajectories
Keywords :
speech recognition; parametric trajectory models; speech process; speech recognition; time-varying covariances; Cepstral analysis; Cognitive science; Covariance matrix; Equations; Gaussian distribution; Hidden Markov models; Polynomials; Random variables; Speech processing; Speech recognition;
Conference_Titel :
Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-3555-4
DOI :
10.1109/ICSLP.1996.607155