engleski

Front-End Signal Processing for Speech Recognition

The evolution of computer technology, including operating systems and applications, resulted in designing intelligent machines that can recognize the spoken word and find out its meaning. Different front-end models have specific processing time required for calculating the same number of coefficients used for pattern recognition. During the years, it has been significantly improved, not only thanks to improvements in algorithms, but also with more processing power of nowadays computers. In this paper we analyze processing time and reconstructed speech quality of the three common front-end methods (Linear Predictive Coding - LPC, Mel-Frequency Cepstrum - MFC, Perceptual Linear Prediction - PLP) for calculating coefficients. Reconstructed speech quality is measured with Perceptual Evaluation of Speech Quality (PESQ) score. It is visible from our analysis that, if required, higher number of coefficients could be used without significant impact on processing time for MFC and PLP coefficients.

speech recognition; front-end; LPC; MFC; PLP; signal processing; PESQ

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