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From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
[article]
2019
arXiv
pre-print
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to get competitive performance, especially on English which has poor grapheme-phoneme correspondence. In this work, we show for the first time that, on English, hybrid ASR systems can in fact model graphemes effectively by leveraging tied context-dependent graphemes, i.e., chenones. Our chenone-based systems significantly
arXiv:1910.01493v2
fatcat:vsaongotkjfjlid7mlp7zl7snu