Ashish Panda

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We investigate the problem of speaker verification in noisy conditions in this paper. Our work is motivated by the fact that environmental noise severely degrades the performance of speaker verification systems. We present a model compensation scheme based on the psychoacoustic principles that adapts the model parameters in order to reduce the training and(More)
  • Ashish Panda
  • 2014
This paper addresses the problem of speaker verification in the presence of additive noise for resource deficient languages. Psychoacoustic model compensation (Psy-Comp) has been shown to impart noise robustness to Gaussian Mixture Model (GMM) based speaker verification systems using Mel Frequency Cepstral Coefficients (MFCCs). This work extends the idea of(More)
In this paper, we address the problem of speech recognition in the presence of additive noise. We investigate the applicability and efficacy of auditory masking in devising a robust front end for noisy features. This is achieved by introducing a masking factor into the Vector Taylor Series (VTS) equations. The resultant first order VTS approximation is used(More)
In this paper, we investigate the applicability and effectiveness of advanced feature compensation techniques in devising a robust front-end for Automatic Speech Recognition (ASR). First, the Vector Taylor Series (VTS) equations are altered by bringing in the auditory masking factor. The resultant VTS approximation is used to compensate the parameters of a(More)
Speaker verification (SV) systems need to be robust to mimicked voices of target speakers as non-target trials to make them usable in critical applications. However, the performance of SV systems for mimicked voice test conditions has not been extensively explored. In an earlier work, we used Amrita SRE database to evaluate the performance of different(More)
This paper addresses the problem of speech recognition in the presence of additive noise. It focuses on Psychoacoustic Model Compensation (Psy-Comp) scheme, which has been shown to be a powerful technique for noise robustness. It has further implemented model domain mean and variance normalization along with Psy-Comp to alleviate channel noise for robust(More)