Papers › FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

29 Jun 2021arXiv:2106.15123archive 2025-07-28

Taejun Bak, Jae-Sung Bae, Hanbin Bae, Young-Ik Kim, Hoon-Young Cho

Methods for modeling and controlling prosody with acoustic features have been proposed for neural text-to-speech (TTS) models. Prosodic speech can be generated by conditioning acoustic features. However, synthesized speech with a large pitch-shift scale suffers from audio quality degradation, and speaker characteristics deformation. To address this problem, we propose a feed-forward Transformer based TTS model that is designed based on the source-filter theory. This model, called FastPitchFormant, has a unique structure that handles text and acoustic features in parallel. With modeling each feature separately, the tendency that the model learns the relationship between two features can be mitigated.

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keonlee9420/FastPitchFormant mentioned on GitHubpytorchMIT report

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Speech SynthesisText to Speechtext-to-speech

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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