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Vector Quantization based Speech Recognition Performance Improvement using Maximum Log Likelihood in Gaussian Distribution
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가우시안 분포에서 Maximum Log Likelihood를 이용한 벡터 양자화 기반 음성 인식 성능 향상

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Type
Academic journal
Author
Journal
The Korea Society of Digital Policy & Management 디지털융복합연구 디지털융복합연구 제16권 제11호 KCI Accredited Journals
Published
2018.1
Pages
335 - 340 (6page)

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Vector Quantization based Speech Recognition Performance Improvement using Maximum Log Likelihood in Gaussian Distribution
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Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

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