Several methods were developed to improve grapheme-to-phoneme （G2P） conversion models for Chinese text-to-speech （TTS） systems. The critical problem of data sparsity was handled by combining approaches. First, a t...Several methods were developed to improve grapheme-to-phoneme （G2P） conversion models for Chinese text-to-speech （TTS） systems. The critical problem of data sparsity was handled by combining approaches. First, a text-selection method was designed to cover as many G2P text corpus contexts as possible. Then, various data-driven modeling methods were used with comparisons to select the best method for each polyphonic word. Finally, independent models were used for some neutral tone words in addition to the normal G2P models to achieve more compact and flexible G2P models. Tests show that these methods reduce the relative errors by 50% for both normal polyphonic words and Chinese neutral tones.展开更多
文摘Several methods were developed to improve grapheme-to-phoneme （G2P） conversion models for Chinese text-to-speech （TTS） systems. The critical problem of data sparsity was handled by combining approaches. First, a text-selection method was designed to cover as many G2P text corpus contexts as possible. Then, various data-driven modeling methods were used with comparisons to select the best method for each polyphonic word. Finally, independent models were used for some neutral tone words in addition to the normal G2P models to achieve more compact and flexible G2P models. Tests show that these methods reduce the relative errors by 50% for both normal polyphonic words and Chinese neutral tones.
