Poonam Bansal

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In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order(More)
In this paper, after an a review of the previous work done in this field, the most frequently used approach using Hidden Markov Model (HMM) is used for implementation for phonetic segmentation. A baseline HMM phonetic segmentation tool is used for segmentation and analysis of speech at phonetic level. The results are approximately same as obtained using(More)
Noise robustness is one of the most challenging problem in automatic speech recognition. The goal of robust feature extraction is to improve the performance of speech recognition in adverse conditions. The mel-scaled frequency cepstral coefficients (MFCCs) derived from Fourier transform and filter bank analysis are perhaps the most widely used front-ends in(More)
This paper presents a new front-end for robust speech recognition. This new front-end scenario focuses on the spectral features of the filtered speech signals in the autocorrelation domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In this paper, a novel method for robust speech extraction is proposed(More)
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