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Recent research shows that the i-vector framework for speaker recognition can significantly benefit from phonetic information. A common approach is to use a deep neural network (DNN) trained for automatic speech recognition to generate a universal background model (UBM). Studies in this area have been done in relatively clean conditions. However, strong background noise is known to severely reduce...
To alleviate the problem of severe degradation of speaker recognition performance under noisy environments because of inadequate and inaccurate speaker-discriminative information, a method of robust feature estimation that can capture both vocal source- and vocal tract-related characteristics from noisy speech utterances is proposed. Spectral subtraction, a simple yet useful speech enhancement technique,...
This paper aims at speaker verification system for Cellular phone transmission for high security purpose. Online processing of voices and verification is achieved in this. Various measures were taken to improve the SNR of the incoming cellular channel signal, as otherwise it would lead to very high degradation in verification performance. A verification percentage of 88.18% is achieved and imposter...
In this paper, a new feature selection method for speaker recognition is proposed to keep the high quality speech frames for speaker modelling and to remove noisy and corrupted speech frames. In order to obtain robust voice activity detection in variety of acoustic conditions, the spectral subtraction algorithm is adopted to estimate the frame power. An energy based frame selection algorithm is then...
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