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Language Identification by Using Syllable-Based Duration Classification on Code-Switching Speech

机译:语音转换中基于音节的时长分类识别语言

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Many approaches to automatic spoken language identification (LID) on monolingual speech are successfully, but LID on the code-switching speech identifying at least 2 languages from one acoustic utterance challenges these approaches. In[6], we have successfully used one-pass approach to recognize the Chinese character on the Mandarin-Taiwanese code-switching speech. In this paper, we introduce a classification method (named syllable-based duration classification) based on three clues: recognized common tonal syllable tonal syllable, the corresponding duration and speech signal to identify specific language from code-switching speech. Experimental results show that the performance of the proposed LID approach on code-switching speech exhibits closely to that of parallel tonal syllable recognition LID system on monolingual speech.
机译:在单语言语音上自动进行自动口语识别(LID)的许多方法都是成功的,但是在代码转换语音上从一种声音中识别至少2种语言的LID挑战了这些方法。在文献[6]中,我们已经成功地使用单通方法来识别普通话-台湾语代码转换语音中的汉字。在本文中,我们基于三个线索介绍了一种分类方法(基于音节的持续时间分类):识别通用音节音节,相应的持续时间和语音信号以从代码转换语音中识别特定语言。实验结果表明,所提出的LID方法在代码转换语音上的性能与在单语言语音上并行音节识别LID系统的性能紧密相关。

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