Emotional Voice Conversion using multitask learning with Text-to-speech
Table 1: WER comparison
['[EMPTY]', 'VC', 'VCTTS-V', 'VCTTS-T', 'TTS']
Word error rate (WER) was computed to measure how our proposed model improves the linguistic consistency of the converted speech. Practically, morphemes were used instead of words since morphemes are considered as recognition units of Korean speech [KwonP03, LeeC04, BangKK18]. Google Cloud Speech-to-Text API transcribed the converted speech, and transcripts were divided into a sequence of morphemes by the Komoran morphological analyzer in KoNLPy [ParkC14_konlpy]. The result shows that VCTTS-V outperforms VC, and WER of VCTTS-T is worse than TTS.