Rathinavelu Chengalvarayan

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In the study reported in this paper, we investigate interactions of front-end feature extraction and back-end classification techniques in hidden Markov model-based (HMMbased) speech recognition. The proposed model focuses on dimensionality reduction of the mel-warped discrete fourier transform (DFT) feature space subject to maximal preservation of speech(More)
In this paper, we extend the maximum likelihood (ML) training algorithm to the minimum classification error (MCE) training algorithm for discriminatively estimating the state-dependent polynomial coefficients in the stochastic trajectory model or the trended hidden Markov model (HMM) originally proposed in [2]. The main motivation of this extension is the(More)
This paper addresses the problem of speech recognition under accent variations in English language. It has been demonstrated in previous research efforts that the multi-transitional model architecture is one of the solutions for robust speech recognition. In this study, we describe an universal hybrid system that is trained with data from American,(More)
We investigate a class of features related to voicing parameters that indicate whether the vocal chords are vibrating. Features describing voicing characteristics of speech signals are integrated with an existing 38-dimensional feature vector consisting of rst and second order time derivatives of the frame energy and of the cepstral coe cients with their(More)
A formulation of the maximum a posteriori (MAP) approach to speaker adaptation is presented with use of the trended or nonstationary-state hidden Markov model (HMM), where the Gaussian means in each HMM state are characterized by time-varying polynomial trend functions of the state sojourn time. Assuming uncorrelatedness among the polynomial coefficients in(More)
Compared to the landline network environment, the wireless environment presents new factors affecting ASR performance (or accuracy). Our goal here is to determine these factors, and their relative importance, and then to devise methods to mitigate them. We approach this goal, first, by conducting a set of experiments where we use a state of the art ASR(More)
In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates(More)
The study presented in this work is a first effort at real-time speech translation of TED talks, a compendium of public talks with different speakers addressing a variety of topics. We address the goal of achieving a system that balances translation accuracy and latency. In order to improve ASR performance for our diverse data set, adaptation techniques(More)
We investigate speech recognition features related to voicing functions that indicate whether the vocal folds are vibrating. We describe two voicing features, periodicity and jitter, and demonstrate that they are powerful voicing discriminators. The periodicity and jitter features and their first and second time derivatives are appended to a standard(More)
In this letter, we report our recent work on applications of the combined maximum likelihood linear regression (MLLR) and the minimum classification error training (MCE) approach to estimating the time-varying polynomial Gaussian mean functions in the trended hidden Markov model (HMM). We call this integrated approach the minimum classification error linear(More)