本文在TMS320C6701EVM 板的基礎上實現一種快速的說話人識別系統。本文提出一種基于段級語音特征的說話人識別的快速算法，該算法在傳統的GMM 算法的基礎上使用段級語音特征對測試語音進行數據量壓縮，以減少計算時間。并基于車比雪夫和不等式提出了基于協方差模型的段級特征的失真測度描述。本文根據實驗選擇了段級特征語音段長度，實驗表明該算法在不顯著影響識別率的基礎上有效地減少了算法延遲，提高了識別速度。
關鍵詞:段級特征; 車比雪夫和不等式; 說話人識別; 數字信號處理芯片
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