Comparison of Indonesian speaker recognition using Vector Quantization and Hidden Markov Model for unclear pronunciation problem | IEEE Conference Publication | IEEE Xplore

Comparison of Indonesian speaker recognition using Vector Quantization and Hidden Markov Model for unclear pronunciation problem


Abstract:

This paper presents a comparison of two classifier methods based on accuracy level in Indonesian speaker recognition for unclear pronunciation problem in a word, simple s...Show More

Abstract:

This paper presents a comparison of two classifier methods based on accuracy level in Indonesian speaker recognition for unclear pronunciation problem in a word, simple sentences, and complete sentences. The first method is Vector Quantization (VQ) based on distortion distance and the second method is Hidden Markov Model (HMM) based on the probability value of the data is observed. Based on the experiments, It can be concluded that HMM method have better accuracy than VQ method especially for pronunciation of simple sentences.
Date of Conference: 03-04 October 2016
Date Added to IEEE Xplore: 13 February 2017
ISBN Information:
Electronic ISSN: 2470-640X
Conference Location: Bandung, Indonesia

References

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