Comparison Of Cepstral And Mel Frequency Cepstral Coefficients For Various Clean And Noisy Speech Signals
Speech is the most natural form of human communication and speech processing has been one of the most exciting areas of the signal processing. The main goal of speech recognition area is to develop techniques and systems for speech input to machine. Speech recognition can be roughly divided into two stages: feature extraction and classification. Although significant advances have been made in speech recognition technology, it is still a difficult problem to design a speech recognition system for speaker-independent, continuous speech. One of the fundamental questions is whether all of the information necessary to distinguish words is preserved during the feature extraction stage. If vital information is lost during this stage, the performance of the following classification stage is inherently crippled and can never measure up to human capability. Thus, this work finds out an improved feature extraction algorithm based on Mel frequency cepstral coefficient analysis. The results show the comparative analysis of various noise signals and their performance measure using SNR and peak power signal.
M.Kalamani, Dr.S.Valarmathy, C.Poonkuzhali, R.Karthiprakash
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