Contribution of spectral shapes to tone perception

In Thai, tonal information is a crucial component for identifying the lexical meaning of a word. Consequently, Thai tone classification can obviously improve performance of Thai speech recognition system. In this article, we therefore reported our study of Thai tone classification. Based on our investigation, most of Thai tone classification studies relied on statistical machine learning approaches, especially the Artificial Neural Network (ANN)-based approach and the Hidden Markov Model (HMM)-based approach. Although both approaches gave reasonable performances, they had some limitations due to their mathematical models. We therefore introduced a novel approach for Thai tone classification using a Hidden Conditional Random Field (HCRF)based approach. In our study, we also investigated tone configurations involving tone features, frequency scaling and normalization techniques in order to fine-tune performances of Thai tone classification. Experiments were conducted in both isolated word scenario and continuous speech scenario. Results showed that the HCRF-based approach with the feature F_dF_aF, ERB-rate scaling and a z-score normalization technique yielded the highest performance and outperformed a baseline using the ANNbased approach, which had been reported as the best for the Thai tone classification, in both scenarios. The best performance of HCRF-based approach provided the error rate reduction of 10.58% and 12.02% for isolated word scenario and continuous speech scenario respectively when comparing with the best result of baselines.

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Contribution of spectral shapes to tone perception

Cited by 2 publications

References 8 publications

Generation of fundamental frequency contours for Thai speech synthesis using tone nucleus model

Generation of fundamental frequency contours for Thai speech synthesis using tone nucleus model

A Hidden Conditional Random Field-Based Approach for Thai Tone Classification

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