Speech recognition using hidden Markov models with multiple‐track polynomial regression functions as nonstationary states

Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states

AbsfractWe propose, implement, and evaluate a class of nonstationary-state hidden Markov models (HMM’s) having each state associated with a distinct polynomial regression function of time plus white Gaussian noise. The model represents the transitional acoustic trajectories of speech in a parametric manner, and includes the standard stationary-state HMM as a special, degenerated case. We develo...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speech emotion recognition using hidden Markov models

This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system. The approach is based on standard speech recognition technology using hidden semi-continuous Markov models. Both the selection of low level features and the design of the recognition system are addressed. Results are given on speaker dependent emotion recognition using the Spanish co...

Speech emotion recognition using hidden Markov models

In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is...