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Missing feature theory (MFT) has been proposed as a solution for robust speech recognition. It improves robustness of speech recognition systems by either ignoring or compensating the unreliable components of feature vectors corrupted mainly by band-limited background noise. Since the local corruption often occurs in the frequency domain and it is smeared by the Discrete Cosine Transform (DCT) used...
The application of Missing Data Technique (MDT) has shown to improve the performance of speech recognition. To apply MDT to cepstral domain, this paper presents a weighted approach to compute the reliability of cepstral feature based on sigmoid function and introduces a weighted distance algorithm. It is deduced that the reliability compensates the Gaussian variance in hidden Markov model (HMM) frame...
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