Supervector pre-processing for PRSVM-based Chinese and Arabic dialect identification
Qian Zhang,Hynek Boril,John H. L. Hansen +2 more
- 26 May 2013
- pp 7363-7367
TL;DR: Variations to supervector pre-processing for phone recognition-support vector machines (PRSVM) based dialect identification are explored and a newly proposed dialect salience measure is applied in supervector dimension selection and compared to a common N-gram frequency based selection.
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Abstract: Phonotactic modeling has become a widely used means for speaker, language, and dialect recognition. This paper explores variations to supervector pre-processing for phone recognition-support vector machines (PRSVM) based dialect identification. The aspects studied are: (i) normalization of supervector dimensions in the pre-squashing stage, (ii) impact of alternative squashing functions, and (iii) N-gram selection for supervector dimensionality reduction. In (i) and (ii), we find that several alternatives to commonly used approaches can provide moderate, yet consistent performance improvements. In (iii), a newly proposed dialect salience measure is applied in supervector dimension selection and compared to a common N-gram frequency based selection. The results show a strong correlation between dialect-salience and frequency of occurrence in N-grams. The evaluations in this study are conducted on a corpus of Chinese dialects, a Pan-Arabic corpus, and a set of Arabic CTS corpora.
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Citations
Natural Language Processing for Dialectical Arabic: A Survey
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TL;DR: This paper presents a wide literature review of natural language processing for dialectical Arabic and identifies relevant contributions that address a specific NLP aspect for a specific dialect.
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Maryam Najafian,Saeid Safavi,Philip Weber,Martin J. Russell +3 more
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TL;DR: This paper demonstrates that the relatively simple i-vector and phonotactic fused system with recognition accuracy of 84.87% outperforms the i- vector fused results reported in literature, by 4.7%.
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Dialect Recognition Based on Unsupervised Bottleneck Features.
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- 20 Aug 2017
TL;DR: An unsupervised BNF extraction diagram is proposed in this study, which is derived from the traditional structure but trained with an estimated phonetic label, all without the need of a secondary transcribed corpus.
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