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Chenglong Jiang

2 accepted papers

2024

Fastmandarin: Efficient Local Modeling for Natural Mandarin Speech Synthesis

ICASSP 2024accepted

Attention-based speech synthesis methods often suffer from dispersed attention across the entire input sequence, resulting in poor local modeling and unnatural Mandarin synthesized speech. To address these issues, we present FastMandarin, a rapid and natural Mandarin speech synthesis framework that…

Cited by 0SourceScholar
2023

SeDepTTS: Enhancing the Naturalness via Semantic Dependency and Local Convolution for Text-to-Speech Synthesis

AAAI 2023technical

Self-attention-based networks have obtained impressive performance in parallel training and global context modeling. However, it is weak in local dependency capturing, especially for data with strong local correlations such as utterances. Therefore, we will mine linguistic information of the origina…