Open AccessReal‐time implementation and performance evaluation of speech classifiers in speech analysis‐synthesis
Author(s) -
Kumar Sandeep
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2019-0364
Subject(s) - speech recognition , computer science , linear predictive coding , classifier (uml) , mel frequency cepstrum , cepstrum , linear prediction , voice activity detection , autocorrelation , speech processing , artificial intelligence , computation , pattern recognition (psychology) , speech coding , feature extraction , mathematics , algorithm , statistics
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.