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.

中文翻译：

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语音分析合成中语音分类器的实时实现和性能评估

在这项工作中，基于自相关函数（ACF），平均幅度差函数（AMDF），倒谱，加权ACF（WACF），零交叉率和信号能量（ZCR-E）的六个浊音/清音语音分类器，以及使用TMS320C6713 DSP入门工具包对神经网络（NN）进行了实时仿真和实现。这些语音分类器已集成到基于线性预测编码的语音分析综合系统中，并根据浊音/清音分类准确度的百分比，语音质量和计算时间对它们的性能进行了比较。浊音/清音分类准确度和语音质量百分比的结果表明，基于NN的语音分类器的性能优于ACF‐，AMDF‐，倒谱，适用于干净和嘈杂环境的基于WACF和ZCR-E的语音分类器。计算时间结果表明，基于AMDF的语音分类器计算简单，因此其计算时间比其他语音分类器要短，而基于NN的语音分类器要比其他分类器长。