Pali Speech Synthesis using HMM

Charoenrattana, Kittikan; Seresangtakul, Pusadee

doi:10.1109/kst51265.2021.9415759

Cited by 3 publications

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“…In Thai text, natural demarcations do not exist between words 4,5 . During Thai text analysis, we need to delineate word boundaries from a continuous sequence of Thai characters, which is a prerequisite for Thai speech synthesis [6][7][8] .A dictionary-based matching disambiguation method has the advantages of being simple and easy to implement 9,10 . However, the matching process requires a large-capacity dictionary, so there will be problems such as the large workload of constructing dictionaries, difficulties of long cycle time, slow cutoff speed, intersection cutoff ambiguity and combination cutoff ambiguity [11][12][13] .…”

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A study of Thai word separation methods based on machine learning

Xiong

Zhao

Duan

2022

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Thai is an analytic, isolated language where monosyllabic words occupy most of the basic vocabulary, and differences in vocabulary and grammar are distinguished by differences in tone. Traditional dictionary-based matching methods have difficulty constructing a complete dictionary. We use four machine learning algorithms, namely, a plain Bayesian classifier, a decision tree classifier, a support vector machine classifier, and a conditional random field, to slice Thai language texts for multiclass problems. In addition, we use the above four machine learning algorithms for a two-class problem slicing of Thai texts. In this study, we found that first, the accuracy, recall and F1 values of Thai text slicing using the conditional random field algorithm in the multiclass classification problem are much higher than those of the plain Bayesian classifier, decision tree classifier and support vector machine classifier. Second, the accuracy, recall, and F1 values of the plain Bayesian algorithm, J48 decision tree algorithm, and support vector machine algorithm are higher than those of the multiclass problem classification in both classes. The accuracy of the conditional random field was improved, but its recall and F1 values decreased significantly. In summary, it can be seen that the conditional random field has better results in Thai word separation.

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