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http://dx.doi.org/10.4218/etrij.2019-0364

Real-time implementation and performance evaluation of speech classifiers in speech analysis-synthesis  

Kumar, Sandeep (Department of Electronics and Communication Engineering, National Institute of Technology)
Publication Information
ETRI Journal / v.43, no.1, 2021 , pp. 82-94 More about this Journal
Abstract
In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.
Keywords
ACF; AMDF; Cepstrum; neural network; real-time system; speech classification; WACF; ZCR-E;
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