Language Recognition for Dialects and Closely Related Languages - PowerPoint PPT Presentation

Apr 07, 2024 •290 likes •322 views

Language Recognition for Dialects and Closely Related Languages NIST OpenLRE 2015 G. Gelly, J.L. Gauvain, L. Lamel, A. Laurent, V.B. Le, A. Messaoudi 1 / 3 Language Recognition for Dialects and Closely Related Languages (G. Gelly, et

Language Recognition for Dialects and Closely Related Languages – NIST OpenLRE 2015 G. Gelly, J.L. Gauvain, L. Lamel, A. Laurent, V.B. Le, A. Messaoudi 1 / 3 – Language Recognition for Dialects and Closely Related Languages (G. Gelly, et al., LIMSI-Vocapia)
Pitch Main objectives Being able to identify the dialect when given excerpts of spoken conversations Benchmark different LID approaches in preparation for NIST LRE15 Techniques studied Phonotactic I-vectors LSTM-RNN Lexical 2 / 3 – Language Recognition for Dialects and Closely Related Languages (G. Gelly, et al., LIMSI-Vocapia)
Pitch Main results LSTM-RNN can lead to a lower LER than I-vectors Phonotactic is the most robust method Phonotactic and LSTM-RNN fusion can lead to an important LER reduction 50 por-brz PHO spa-lac 45 RNN spa-eur IVC spa-car 40 LEX qsl-rus RNN+PHO qsl-pol 35 RNN+PHO+IVC fre-hat system output fre-waf RNN+PHO+IVC+LEX eng-sas average LER 30 eng-usg eng-gbr 25 zho-wuu zho-cdo zho-cmn 20 zho-yue ara-arb 15 ara-ary ara-apc 10 ara-acm ara-arz 5 ara-arz ara-acm ara-apc ara-ary ara-arb zho-yue zho-cmn zho-cdo zho-wuu eng-gbr eng-usg eng-sas fre-waf fre-hat qsl-pol qsl-rus spa-car spa-eur spa-lac por-brz 0 [0;5] ]5;10] ]10;20] ]20;30] > 30 all speech duration in seconds target dialect 3 / 3 – Language Recognition for Dialects and Closely Related Languages (G. Gelly, et al., LIMSI-Vocapia)

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