{"title":"利用声学特征消歧孤立的曼尼普尔音调对比词对","authors":"Thiyam Susma Devi, Pradip K. Das","doi":"10.1145/3643830","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":"38 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Disambiguation of Isolated Manipuri Tonal Contrast Word Pairs using Acoustic Features\",\"authors\":\"Thiyam Susma Devi, Pradip K. Das\",\"doi\":\"10.1145/3643830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. 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引用次数: 0
摘要
曼尼普尔语是一种资源匮乏的藏缅语调语言,主要在印度东北部的曼尼普尔邦使用。音调识别对于音调语言的语音理解至关重要,因为音调决定了单词的含义。如果将强大的音调检测系统提供的音调信息包括在内,这些语言的自动语音识别功能就能发挥得更好。虽然对普通话、泰语、粤语和越南语等声调语言进行了大量研究,但在探索曼尼普里语方面还存在明显差距。为了填补这一空白,这项工作扩展了我们之前开发的手工制作语音语料库 ManiTo,该语料库由孤立的曼尼普尔语声调对比词对组成,用于研究曼尼普尔语的声调。这一扩展包括来自 20 位母语人士的贡献。初步研究结果证实,曼尼普尔语有两种独特的音调,即 "下降 "和 "水平"。研究随后进行了全面的声学特征分析。研究了基于音高轮廓、抖动和微光测量的两组特征,以区分曼尼普里语的两种音调。支持向量机、长短期记忆、随机森林和 k 近邻是验证所选特征集的分类器。结果表明,第二组特征始终优于第一组特征,尤其是在使用随机森林分类器时,表现出更高的准确性,这为进一步提高低资源音调语言曼尼普尔语的语音识别技术提供了宝贵的见解。
Disambiguation of Isolated Manipuri Tonal Contrast Word Pairs using Acoustic Features
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.
期刊介绍:
The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to:
-Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc.
-Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc.
-Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition.
-Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc.
-Machine Translation involving Asian or low-resource languages.
-Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc.
-Information Extraction and Filtering: including automatic abstraction, user profiling, etc.
-Speech processing: including text-to-speech synthesis and automatic speech recognition.
-Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc.
-Cross-lingual information processing involving Asian or low-resource languages.
-Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.