本文在TMS320C6701EVM 板的基础上实现一种快速的说话人识别系统。本文提出一种基于段级语音特征的说话人识别的快速算法，该算法在传统的GMM 算法的基础上使用段级语音特征对测试语音进行数据量压缩，以减少计算时间。并基于车比雪夫和不等式提出了基于协方差模型的段级特征的失真测度描述。本文根据实验选择了段级特征语音段长度，实验表明该算法在不显著影响识别率的基础上有效地减少了算法延迟，提高了识别速度。关键词:段级特征; 车比雪夫和不等式; 说话人识别; 数字信号处理芯片Abstract: This paper realize a fast speaker recognize system based on TMS320C6701 EVM board. This paper proposes a fast speaker recognize algorithm based on utterance level feature. Within the system, the algorithm’s framework is GMM and compress data using utterance level feature. The paper also gives out utterance level covariance distortion measure by using Chebyshev’s summation inequalities. This paper chooses the best utterance according to the experiments. Experiments results show that this algorithm efficiently reduce computation delay and fast recognition speed.Key words: Utterance level feature; Chebyshev’s summation inequalities; Speaker recognition; DSP
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