用模擬退火算法實現(xiàn)語音識別中的矢量量化
ANNEALING VECTOR QUANIZATION IN SPEECH RECOGNITION
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摘要: 矢量量化在語音識別中有著重要的作用。經(jīng)典的K均值算法收斂速度快,但極易收斂于局部最佳點;其它的一系列改進算法在克服其局部收斂問題的同時,又顯著增加了運算量。本文提出了用模擬退火算法實現(xiàn)語音識別中的矢量量化過程,能夠較好地協(xié)調運算量和收斂質量之間的矛盾。文章討論了具體算法,并給出了實驗數(shù)據(jù)。結果表明該方法的綜合性能優(yōu)于現(xiàn)有算法,具有較高的實用價值。Abstract: Vector quantization plays an important role in speech recognition.Traditional K-means algorithm owns the advantage of fast convergence, but it is difficult to get the global optimal result.Some modified algorithms have been proposed to overcome this drawback,but they also increase the computation greatly.In this papsr,a new algorithm which is based on annealing algorithm is proposed to compromise the contradiction.In the rest of the paper,the details of the algorithm and related experiments are given.The results demonstrate the algorithm is more effective than other methods.
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