December 14, 2019
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
IEEE Automatic Speech Recognition and Understanding Workshop
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.
By: Duc Le, Xiaohui Zhang, Weiyi Zhang, Christian Fuegen, Geoffrey Zweig, Michael L. Seltzer
Natural Language Processing & Speech