DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level and Utterance-Level Acoustic Representation Learning
Takaaki Saeki, Kentaro Tachibana, Ryuichi Yamamoto · Interspeech 2022 · 2022
Most text-to-speech (TTS) methods use high-quality speech corpora recorded in a well-designed environment, incurring a high cost for data collection.To solve this problem, existing noise-robust TTS methods are intended to use noisy speech corpora as training data.However, they only address either timeinvariant or time-variant noises.We propose a degradationrobust TTS method, which can be trained on speech corpora that contain both additive noises and environmental distortions.It jointly represents the time-variant additive noises with a framelevel encoder and the time-invariant environmental distortions with an utterance-level encoder.We also propose a regularization method to attain clean environmental embedding that is disentangled from the utterance-dependent information such as linguistic contents and speaker characteristics.Evaluation results show that our method achieved significantly higher-quality synthetic speech than previous methods in the condition including both additive noise and reverberation.