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- W4319862439 abstract "Multilingual Automatic Speech Recognition (ASR) for Indian languages is an obvious technique for leveraging their similarities. We present a detailed analysis of how phonological similarities and differences between languages affect Time Delay Neural Network (TDNN) and End-to-End (E2E) ASR. To study this, we select genealogically similar pairs from five Indian languages and train bilingual acoustic models. We compare these against corresponding monolingual acoustic models and find similar phoneme distributions within speech to be the primary factor for improving model performance, with phoneme overlap being secondary. The influence of phonological properties on performance is visible in both cases. Word Error Rate (WER) of E2E decreased by a median of 2.35%, and upto 8.5% when the phonological similarity was greatest. WER of TDNN increased by 11.69% when the similarity was lowest. Thus, it is clear that the choice of supplementary language is important for model performance." @default.
- W4319862439 created "2023-02-11" @default.
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- W4319862439 date "2023-01-09" @default.
- W4319862439 modified "2023-09-26" @default.
- W4319862439 title "How do Phonological Properties Affect Bilingual Automatic Speech Recognition?" @default.
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- W4319862439 doi "https://doi.org/10.1109/slt54892.2023.10023156" @default.
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