Vataya Boonpiam

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This paper proposes an efficient acoustic model adaptation method based on the use of simulated-data in maximum likelihood linear regression (MLLR) adaptation for robust speech recognition. Online MLLR adaptation is an unsupervised process which requires an input speech with phone labels transcribed automatically. Instead of using only the input signal in(More)
This paper proposes a new environmental noise classification using principal component analysis (PCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the identified noise specific acoustic model. The proposed model applies PCA to a set of noise features, and results from PCA(More)
This paper proposes the use of tree-structured model selection and simulated-data in maximum likelihood linear regression (MLLR) adaptation for environment and speaker robust speech recognition. The objective of this work is to solve major problems in robust speech recognition system, namely unknown speaker and unknown environmental noise. The proposed(More)
This paper proposed a novel method for F0 modeling in under-resourced tonal languages. Conventional statistical models require large training data which are deficient in many languages. In tonal languages, different syllabic tones are represented by different F0 shapes, some of them are similar across languages. With cross-language F0 contour mapping, we(More)
This article investigates as an early work on speech recognition of Thai named-entities, which is a crucial out-of-vocabulary word problem in broadcast news transcription. Motivated by an analysis on Thai-name structure, a statistical class-based language model is applied on multiple-sized subword units with a constraint on subword positions. Subwords can(More)
This paper proposes a combination of simulated data adaptation and piecewise linear transformation (PLT) for robust continuous speech recognition. The original PLT selects an appropriate acoustic model using tree-structured HMMs and the acoustic model is adapted by the input speech in an unsupervised scheme. This adaptation can improve the acoustic model if(More)
This paper proposes an environmental noise classification method using kernel principal component analysis (KPCA) for robust speech recognition. Once the type of noise is identified, speech recognition performance can be enhanced by selecting the identified noise specific acoustic model. The proposed model applies KPCA to a set of noise features such as(More)
This paper proposes a novel approach called noise-cluster HMM interpolation for robust speech recognition. The approach helps alleviating the problem of speech recognition under noisy environments not trained in the system. In this method, a new HMM is interpolated from existing noisy-speech HMMs that are best matched to the input speech. This process is(More)
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