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標題: 利用Multiple Common Vector及Dynamic Time Warping於特定語者中文單音辨識
Using the Method of Multiple Common Vector and Dynamic Time Warping to Recognize Isolated Mandarin Word for Speaker-Dependent System
作者: 林子傑
Lin, Tzu-Chieh
關鍵字: Common Vector;共同向量;Principal Component Analysis;Dynamic Time Warping;主成份分析;動態時間軸校正法
出版社: 應用數學系所
本篇論文主要是探討50個國字單音的辨識,首先利用主成份分析與共同向量的關係來建構出語音模型,之後在辨識比對的部分,我們將時間的因素考慮進去,所以試著加入動態時間軸校正法,觀察其能否提升辨識率;包括了動態時間軸校正法,本論文討論的其他四個實驗因子:「音框數」、「分群數」、「特徵向量個數」及「語音特徵參數」,希望能找出在何種情況下50個字能具有不錯的鑑別度。而本論文的實驗結果,辨識50個字時,最高辨識率可達97.33 %

This paper is to discuss the speech recognition of 50 isolated mandarin words. First, we use the relationship between principal component analysis and common vector to construct the speech model. Then we will take into account the time factor and attempt to join the dynamic time warping to improve the rate of recognition. Including dynamic time warping, we also consider the other four experimental factors in this paper: "the number of frame", "the number of cluster", "the number of eigenvector", and "speech feature extraction". We hope to find out which circumstances for the recognition of 50 words would be the best. And the maximum rate of recognition attains 97.33 % on the 50 words.
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