Acoustic Features for Hidden Conditional Random Fields--Based Thai Tone Classification

Kertkeidkachorn, Natthawut; Punyabukkana, Proadpran; Suchato, Atiwong

doi:10.1145/2833088

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…More than 90 million people speak Kra-dai languages and Thai is the most widely spoken Kra-dai language. However, most of the existing authorship identifcation solutions are designed for English [15,16,18]. These solutions are not directly applicable to Thai due to linguistic diferences between English and Thai.…”

Section: Introductionmentioning

confidence: 99%

“…These solutions are not directly applicable to Thai due to linguistic diferences between English and Thai. For example, unlike English [15,16,18], (i) Thai has 44 consonants and 4 tone marks; (ii) Thai has 18 vowel symbols that create many compound vowels, and few special symbols; (iii) Thai text samples do not have word/sentence boundaries; and (iv) the frst person pronounce in Thai can be gender-specifc and gender-neutral (i.e. men tends to use the former and women tends to use the later) [18].…”

Section: Introductionmentioning

confidence: 99%

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StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Disambiguation of Isolated Manipuri Tonal Contrast Word Pairs Using Acoustic Features

Devi,

Das

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Manipuri is a low-resource, Tibeto-Burman tonal language spoken mainly in Manipur, a northeastern state of India. Tone identification is crucial to speech comprehension for tonal languages, where tone defines the word’s meaning. Automatic Speech Recognition for those languages can perform better by including tonal information from a powerful tone detection system. While significant research has been conducted on tonal languages like Mandarin, Thai, Cantonese and Vietnamese, a notable gap exists in exploring Manipuri within this context. To address this gap, this work expands our previously developed handcrafted speech corpus, ManiTo, which comprises of isolated Manipuri tonal contrast word pairs to study the tones of Manipuri. This extension includes contributions from twenty native speakers. Preliminary findings have confirmed that Manipuri has two unique tones, Falling and Level. The study then conducts a comprehensive acoustic feature analysis. Two sets of features based on Pitch contours, Jitter and Shimmer measurements are investigated to distinguish the two tones of Manipuri. Support Vector Machine, Long Short-Term Memory, Random Forest and k-Nearest Neighbors are the classifiers adopted to validate the selected feature sets. The results indicate that the second set of features consistently outperformed the first set, demonstrating higher accuracy, particularly when utilizing the Random Forest classifier, which provides valuable insights for further advancements in speech recognition technology for low-resource tonal language Manipuri.

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Acoustic Features for Hidden Conditional Random Fields--Based Thai Tone Classification

Cited by 2 publications

References 18 publications

StyloThai:

StyloThai:

Disambiguation of Isolated Manipuri Tonal Contrast Word Pairs Using Acoustic Features

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