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The Speech Recognition Based on Error-Correcting Output Code and Support Vector Machine

Author: LiZuo
Tutor: ZhangXueYing
School: Taiyuan University of Technology
Course: Communication and Information System
Keywords: Speech recognition Support Vector Machine Error-correcting output coding algorithm Hadamard ECOC Hadamard ECOC sparse
CLC: TN912.34
Type: Master's thesis
Year: 2011
Downloads: 53
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Abstract


With the increase in demand for human-computer interaction , speech recognition has become a hot topic of research , and broad development prospects . The support vector machine is used to solve the multi- class classification has many advantages , it is an important tool in the study of speech recognition . The support vector machine is the essence of a planning optimization problem , it can find the global optimum , to overcome the deficiencies of the traditional methods . Support vector machine , however , is mainly used to solve the problem of binary classification , and how it applies to multi - class classification , researchers have put forward a number of algorithms : one-on-one \acyclic graph method and ECOC algorithm . In this thesis, a combination of speech recognition system based on the the ECOC algorithm and support vector machine , the full text of the following major elements : (1) in-depth study of the error-correcting output coding algorithm . ECOC algorithm has some of the advantages of their own , can classification error can be corrected to a certain extent , thereby giving a more accurate result of the discrimination . (2) the ECOC algorithm and support vector machine combined with voice recognition systems , the decoder decoding based index lost constructed in this article . Application of a variety of encoding to perform voice recognition experiments . (3) This article uses the Hadamard ECOC , the algorithm is better than the performance of the identification of the other error-correcting output coding . However, when a large number of categories , the algorithm will be more time - consuming to training to identify network . To solve this problem , this paper Hadamard ECOC algorithm based on constructed sparse Hadamard ECOC algorithm , that is 1 / 2 sparse Hadamard ECOC algorithm and 1 /3 of the sparse Hadamard Error Correcting Output coding algorithm . The experimental results show that the more the number of categories of speech recognition , these two algorithms varying degrees to reduce training time while maintaining a high recognition rate .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing > Speech Recognition and equipment
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