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Fuzzy-based algorithm for Fongbe continuous speech segmentation

机译：基于模糊的Fongbe连续语音分割算法

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摘要

Text-independent speech segmentation is a challenging topic in computer-based speech recognition systems. This paper proposes a novel time-domain algorithm based on fuzzy knowledge for continuous speech segmentation task via a nonlinear speech analysis. Short-term energy, zero-crossing rate and the singularity exponents are the time-domain features that we have calculated in each point of speech signal in order to exploit relevant information for generating the significant segments. This is down for the phoneme or syllable identification and the transition fronts. Fuzzy logic technique helped us to fuzzify the calculated features into three complementary sets namely: low, medium, high and to perform a matching phase using a set of fuzzy rules. The outputs of our proposed algorithm are silence, phonemes, or syllables. Once evaluated, our algorithm produced the best performances with efficient results on Fongbe language (an African tonal language spoken especially in Benin, Togo and Nigeria).
机译：在基于计算机的语音识别系统中，与文本无关的语音分割是一个具有挑战性的主题。通过非线性语音分析，提出了一种基于模糊知识的连续语音分割任务时域算法。短期能量，过零率和奇异指数是我们在语音信号的每个点中计算出的时域特征，以便利用相关信息来生成重要片段。对于音素或音节识别和过渡音而言，此功能关闭。模糊逻辑技术帮助我们将计算出的特征模糊化为三个互补集合，即低，中，高，并使用一组模糊规则执行匹配阶段。我们提出的算法的输出是静音，音素或音节。经过评估，我们的算法在Fongbe语言（非洲贝宁，多哥和尼日利亚特别使用的非洲声调语言）上产生了最佳性能，并获得了有效的结果。

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