2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)最新文献
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295012
Yu Mon Aye, Sint Sint Aung
A lot of information related to several commercial application available online which can be used to provide the guidance and suggestions to possible new customers. People desire to distribute the opinions and state the sentiments in their own language. Sentiment analyzers developed for English language, are not workable for Myanmar language. Mining sentiments in Myanmar text come with a lot of issues and challenges. The direction of the sentiment is highly depend on the context of sentiment text. Thus, it is significant challenge to consider contextual lexical information in order to correctly classify the polarity. This paper aims to improve the existing challenges problem of language and analyze the sentiment classification of food and restaurants domain by using contextual analysis with lexicon based approach in Myanmar text reviews. The effect of intensifier, negations and objective words are important role in the context of sentiment orientation. This paper addresses sentiment classification for Myanmar Language and overcome one of the problems of language specific challenges. The accuracy of the proposed system is higher than the classification without using context information (negation, intensifier and objective words). Overall accuracy of the proposed system is 92% and weighted average F-measure for imbalance class of 1200 reviews is 0.93.
{"title":"Contextual Lexicon Based Sentiment Analysis in Myanmar Text Reviews","authors":"Yu Mon Aye, Sint Sint Aung","doi":"10.1109/O-COCOSDA50338.2020.9295012","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295012","url":null,"abstract":"A lot of information related to several commercial application available online which can be used to provide the guidance and suggestions to possible new customers. People desire to distribute the opinions and state the sentiments in their own language. Sentiment analyzers developed for English language, are not workable for Myanmar language. Mining sentiments in Myanmar text come with a lot of issues and challenges. The direction of the sentiment is highly depend on the context of sentiment text. Thus, it is significant challenge to consider contextual lexical information in order to correctly classify the polarity. This paper aims to improve the existing challenges problem of language and analyze the sentiment classification of food and restaurants domain by using contextual analysis with lexicon based approach in Myanmar text reviews. The effect of intensifier, negations and objective words are important role in the context of sentiment orientation. This paper addresses sentiment classification for Myanmar Language and overcome one of the problems of language specific challenges. The accuracy of the proposed system is higher than the classification without using context information (negation, intensifier and objective words). Overall accuracy of the proposed system is 92% and weighted average F-measure for imbalance class of 1200 reviews is 0.93.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295022
Bin Li, Yuan Jia
In this paper, we present a study on the phonetic realization of information structure for production of Chinese reading text by depression patients in comparison with normal people. 16 depression patients and 4 normal people were analyzed in this paper. RefLex was selected as the annotation scheme, which differentiates information structure on the lexical and referential levels. Duration and pitch range were selected as the main phonetic parameters. The main findings are as follows. Depression patients can distinguish new, given and accessible information through duration and pitch range. When the degree of information activation increases, patients tend to expand the duration and pitch range on both levels. Further, the phonetic distinction between depression patients and normal people mainly emerges in term of pitch range.
{"title":"The Prosodic Realization of Information Structure for Chinese Discourse Production by Depression Patients","authors":"Bin Li, Yuan Jia","doi":"10.1109/O-COCOSDA50338.2020.9295022","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295022","url":null,"abstract":"In this paper, we present a study on the phonetic realization of information structure for production of Chinese reading text by depression patients in comparison with normal people. 16 depression patients and 4 normal people were analyzed in this paper. RefLex was selected as the annotation scheme, which differentiates information structure on the lexical and referential levels. Duration and pitch range were selected as the main phonetic parameters. The main findings are as follows. Depression patients can distinguish new, given and accessible information through duration and pitch range. When the degree of information activation increases, patients tend to expand the duration and pitch range on both levels. Further, the phonetic distinction between depression patients and normal people mainly emerges in term of pitch range.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130649479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295014
Sann Su Su Yee, K. Soe
Spoken language understanding (SLU) is an essential element of any dialogue system to understand the language where dialogue act (DA) recognition is also critical aspects of pre-processing step for speech understanding and dialogue system. This paper proposes a deep learning-based DA model which use a deep recurrent neural network (RNN) with bi-directional long short-term memory (Bi-LSTM). The model mainly consists of a word-encode layer, a Bi-LSTM layer, and a softmax layer. For corpus preparation, we collected and annotated a large dialog act annotation corpus, which is called MmTravel (Myanmar Travel) corpus, on travel domain human-human conversations dataset (consists of 80k utterances). This paper reports analysis and comparison of proposed model Bi-LSTM with RNN, LSTM, and baseline SVM model. Experiments on the dataset is shown that our proposed DA model performs better than our previous work, support vector machine (SVM) models, which achieve an improvement of more than 2% accuracy increase in classification on the dataset.
{"title":"Myanmar Dialogue Act Recognition Using Bi-LSTM RNN","authors":"Sann Su Su Yee, K. Soe","doi":"10.1109/O-COCOSDA50338.2020.9295014","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295014","url":null,"abstract":"Spoken language understanding (SLU) is an essential element of any dialogue system to understand the language where dialogue act (DA) recognition is also critical aspects of pre-processing step for speech understanding and dialogue system. This paper proposes a deep learning-based DA model which use a deep recurrent neural network (RNN) with bi-directional long short-term memory (Bi-LSTM). The model mainly consists of a word-encode layer, a Bi-LSTM layer, and a softmax layer. For corpus preparation, we collected and annotated a large dialog act annotation corpus, which is called MmTravel (Myanmar Travel) corpus, on travel domain human-human conversations dataset (consists of 80k utterances). This paper reports analysis and comparison of proposed model Bi-LSTM with RNN, LSTM, and baseline SVM model. Experiments on the dataset is shown that our proposed DA model performs better than our previous work, support vector machine (SVM) models, which achieve an improvement of more than 2% accuracy increase in classification on the dataset.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125187313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295011
N. Choudhary, D. G. Rao
This paper introduces the first set of speech corpora released in 2019 by the Linguistic Data Consortium for Indian Languages (LDC-IL), a scheme under the Department of Higher Education, Ministry of Human Resource Development, Government of India. The datasets include a total of 13 scheduled languages of India, collected in various environments across length and breadth of the vast country, from a total of 5662 speakers of different age-groups with a total size of more than 1552 hours. The dataset is still growing as we prune them and make them ready for release. Unique language corpus is usually the largest available at present for these languages. Established in 2008, on the lines of the LDC of University of Pennsylvania, the LDC-IL has worked for over 10 years on various types language resources, including building the speech corpora. LDC-IL is a fully government funded project implemented by CIIL, Mysuru. Due to some restraints in the government business such as cost analysis and copyright issues, it took rather a long time to release the LDC-IL dataset for the public use. This paper gives a brief of the raw speech corpora now released and ready for public use (both commercial and non-commercial purposes). It also discusses how the two major bottlenecks of copyright and costing was addressed which held up the release of these datasets for several years.
{"title":"The LDC-IL Speech Corpora","authors":"N. Choudhary, D. G. Rao","doi":"10.1109/O-COCOSDA50338.2020.9295011","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295011","url":null,"abstract":"This paper introduces the first set of speech corpora released in 2019 by the Linguistic Data Consortium for Indian Languages (LDC-IL), a scheme under the Department of Higher Education, Ministry of Human Resource Development, Government of India. The datasets include a total of 13 scheduled languages of India, collected in various environments across length and breadth of the vast country, from a total of 5662 speakers of different age-groups with a total size of more than 1552 hours. The dataset is still growing as we prune them and make them ready for release. Unique language corpus is usually the largest available at present for these languages. Established in 2008, on the lines of the LDC of University of Pennsylvania, the LDC-IL has worked for over 10 years on various types language resources, including building the speech corpora. LDC-IL is a fully government funded project implemented by CIIL, Mysuru. Due to some restraints in the government business such as cost analysis and copyright issues, it took rather a long time to release the LDC-IL dataset for the public use. This paper gives a brief of the raw speech corpora now released and ready for public use (both commercial and non-commercial purposes). It also discusses how the two major bottlenecks of copyright and costing was addressed which held up the release of these datasets for several years.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117141933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295015
A. M. Mon, K. Soe
Named entity (NE) transliteration is mainly a phonetically based transcription of names across languages using different writing systems. For the Myanmar language, robust transliteration of named entities is still a challenging task, because of the complex writing system and the lack of data. The Myanmar NE transliteration dictionary has so far developed over 135,255 NE instance pairs of western person, organization and place names. We apply statistical experiments on Phrase-based statistical machine translation (PBSMT) model using 2-Grams, 3-Grams, 4-Grams, 5-Grams and 6-Grams language models in decoding. Different units in the Myanmar script, i.e., characters and syllables are compared. We perform experiments on 1,000 test data set and 1,000 development data set of our proposed dictionary and measure the performance of our system applying bilingual evaluation understudy (BLEU) score. We discuss detailed observations of our experiments in this paper. According to the evaluations, we got the significant results on syllable unit for Myanmar (Myan) to English (Eng) transliteration direction with 89.3% BLEU score and on character unit for English (Eng) to Myanmar (Myan) transliteration direction with 82.0% BLEU score.
{"title":"Phrase-Based Named Entity Transliteration on Myanmar-English Terminology Dictionary","authors":"A. M. Mon, K. Soe","doi":"10.1109/O-COCOSDA50338.2020.9295015","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295015","url":null,"abstract":"Named entity (NE) transliteration is mainly a phonetically based transcription of names across languages using different writing systems. For the Myanmar language, robust transliteration of named entities is still a challenging task, because of the complex writing system and the lack of data. The Myanmar NE transliteration dictionary has so far developed over 135,255 NE instance pairs of western person, organization and place names. We apply statistical experiments on Phrase-based statistical machine translation (PBSMT) model using 2-Grams, 3-Grams, 4-Grams, 5-Grams and 6-Grams language models in decoding. Different units in the Myanmar script, i.e., characters and syllables are compared. We perform experiments on 1,000 test data set and 1,000 development data set of our proposed dictionary and measure the performance of our system applying bilingual evaluation understudy (BLEU) score. We discuss detailed observations of our experiments in this paper. According to the evaluations, we got the significant results on syllable unit for Myanmar (Myan) to English (Eng) transliteration direction with 89.3% BLEU score and on character unit for English (Eng) to Myanmar (Myan) transliteration direction with 82.0% BLEU score.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121952248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295019
Y. Liao, Chia-Yu Chang, Hak-Khiam Tiun, Huang-Lan Su, Hui-Lu Khoo, Jane S. Tsay, Le-Kun Tan, Peter Kang, Tsun-guan Thiann, Un-Gian Iunn, Jyh-Her Yang, Chih-Neng Liang
Taiwanese (a.k.a. Taiwanese Hokkien, Hoklo, Taigi, Southern Min or Min-Nan) is an endangered language, because the domination of Mandarin, the number of Taiwanese speakers continues to drop, especially among the youth generations. In addressing this problem, a Taiwanese speech-enabled human-computer interface for supporting people's daily life is essential. Therefore, a Formosa Speech in the Wild (FSW) project was established to collect a large-scale Taiwanese speech across Taiwan (TAT) corpus to boost the development of Taiwanese speech recognition (TSR). A Formosa Speech Recognition Challenge 2020 (FSR-2020) was also hosted to promote the corpus as well as to evaluate the performance of state-of-the-art TSR systems. This paper briefly introduces TAT corpus and FSR-2020 challenge, presents the provided data profile, evaluation plan and reports experimental baseline results. A subset of TAT corpus, TAT-Vol1, is given away for free for all participants (non-commercial license), and its corresponding Kaldi baseline recipes have been published online. Experimental results have showed that the combination of TAT corpus and the baseline recipes is a good resource pack for TSR research and development.
{"title":"Formosa Speech Recognition Challenge 2020 and Taiwanese Across Taiwan Corpus","authors":"Y. Liao, Chia-Yu Chang, Hak-Khiam Tiun, Huang-Lan Su, Hui-Lu Khoo, Jane S. Tsay, Le-Kun Tan, Peter Kang, Tsun-guan Thiann, Un-Gian Iunn, Jyh-Her Yang, Chih-Neng Liang","doi":"10.1109/O-COCOSDA50338.2020.9295019","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295019","url":null,"abstract":"Taiwanese (a.k.a. Taiwanese Hokkien, Hoklo, Taigi, Southern Min or Min-Nan) is an endangered language, because the domination of Mandarin, the number of Taiwanese speakers continues to drop, especially among the youth generations. In addressing this problem, a Taiwanese speech-enabled human-computer interface for supporting people's daily life is essential. Therefore, a Formosa Speech in the Wild (FSW) project was established to collect a large-scale Taiwanese speech across Taiwan (TAT) corpus to boost the development of Taiwanese speech recognition (TSR). A Formosa Speech Recognition Challenge 2020 (FSR-2020) was also hosted to promote the corpus as well as to evaluate the performance of state-of-the-art TSR systems. This paper briefly introduces TAT corpus and FSR-2020 challenge, presents the provided data profile, evaluation plan and reports experimental baseline results. A subset of TAT corpus, TAT-Vol1, is given away for free for all participants (non-commercial license), and its corresponding Kaldi baseline recipes have been published online. Experimental results have showed that the combination of TAT corpus and the baseline recipes is a good resource pack for TSR research and development.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122012750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295008
Joyshree Chakraborty, Priyankoo Sarmah, K. Samudravijaya
Bodo, Dimasa, Rabha and Tiwa are languages of the Tibeto-Burman language family. These languages are spoken in north-east India and surrounding areas. Bodo is also one of the 22 official languages of the Government of India. Consequently, spoken language systems had been developed for Bodo. In contrast, similar systems for the other languages are yet to be developed. Here, we present the details of an automatic Language Identification (LID) system that identifies the language of an input speech file without using phonetic information. The text-independent LID system was implemented using Gaussian mixture model with Mel-Frequency Cepstral Coefficients (MFCCs) as features. A 3-fold cross validation methodology was adopted to assess the performance of the system. The accuracy of the LID system was the highest when suprasegmental features were used in addition to segmental features. The best LID system, using a 62-dimensional feature vector consisting of 13 MFCCs and 49 shifted delta coefficients, yields 92.7% accuracy when the duration of the test data is 3 seconds.
{"title":"Spoken Language Identification of Four Tibeto-Burman languages","authors":"Joyshree Chakraborty, Priyankoo Sarmah, K. Samudravijaya","doi":"10.1109/O-COCOSDA50338.2020.9295008","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295008","url":null,"abstract":"Bodo, Dimasa, Rabha and Tiwa are languages of the Tibeto-Burman language family. These languages are spoken in north-east India and surrounding areas. Bodo is also one of the 22 official languages of the Government of India. Consequently, spoken language systems had been developed for Bodo. In contrast, similar systems for the other languages are yet to be developed. Here, we present the details of an automatic Language Identification (LID) system that identifies the language of an input speech file without using phonetic information. The text-independent LID system was implemented using Gaussian mixture model with Mel-Frequency Cepstral Coefficients (MFCCs) as features. A 3-fold cross validation methodology was adopted to assess the performance of the system. The accuracy of the LID system was the highest when suprasegmental features were used in addition to segmental features. The best LID system, using a 62-dimensional feature vector consisting of 13 MFCCs and 49 shifted delta coefficients, yields 92.7% accuracy when the duration of the test data is 3 seconds.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132175318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295043
Nway Nway Han, A. Thida
Word alignment is an essential task for every Statistical Machine Translation (SMT) system. An alignment is the arrangement of two or more alignments between the parallel sentences. The problem of word alignment in SMT is to find the strong alignment in the corresponding sentence pairs. Moreover, popular word alignment system (GIZA++) needs improvement in Myanmar-English machine translation because Myanmar is inflected, and it is also a language scarce resource. For this reason, this paper presents the idea of word alignment system by adding the extra resources: word and Name Entity Recognition (NER) translation pairs to the existing training data to improve the word alignment system. Experimental results show that the proposed word alignment system reduces the Alignment Error Rate (AER) than baseline.
{"title":"Improved Word Alignment System for Myanmar-English Machine Translation","authors":"Nway Nway Han, A. Thida","doi":"10.1109/O-COCOSDA50338.2020.9295043","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295043","url":null,"abstract":"Word alignment is an essential task for every Statistical Machine Translation (SMT) system. An alignment is the arrangement of two or more alignments between the parallel sentences. The problem of word alignment in SMT is to find the strong alignment in the corresponding sentence pairs. Moreover, popular word alignment system (GIZA++) needs improvement in Myanmar-English machine translation because Myanmar is inflected, and it is also a language scarce resource. For this reason, this paper presents the idea of word alignment system by adding the extra resources: word and Name Entity Recognition (NER) translation pairs to the existing training data to improve the word alignment system. Experimental results show that the proposed word alignment system reduces the Alignment Error Rate (AER) than baseline.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132451135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/o-cocosda50338.2020.9294997
Nathaniel Oco, Sambal Botolan, •. L. Archives, Philippine Hokkien
This article consists only of a collection of slides from the author's conference presentation.
本文仅由作者在会议上发表的一些幻灯片组成。
{"title":"2020 Philippine Country Report","authors":"Nathaniel Oco, Sambal Botolan, •. L. Archives, Philippine Hokkien","doi":"10.1109/o-cocosda50338.2020.9294997","DOIUrl":"https://doi.org/10.1109/o-cocosda50338.2020.9294997","url":null,"abstract":"This article consists only of a collection of slides from the author's conference presentation.","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127123502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-05DOI: 10.1109/O-COCOSDA50338.2020.9295037
{"title":"O-COCOSDA 2020 Tthailand Report November 2020","authors":"","doi":"10.1109/O-COCOSDA50338.2020.9295037","DOIUrl":"https://doi.org/10.1109/O-COCOSDA50338.2020.9295037","url":null,"abstract":"","PeriodicalId":385266,"journal":{"name":"2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125619886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
2020 23rd Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardisation of Speech Databases and Assessment Techniques (O-COCOSDA)
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