Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration
This addresses the challenge of seamless speech insertion for audio editing applications, offering a zero-shot solution that eliminates the need for parallel training data, though it is incremental as it builds on existing TTS and editing concepts.
The paper tackles the problem of text-based speech editing for audio narration by proposing a one-stage context-aware framework that generates natural and coherent target speech without any training data for the target speaker, achieving satisfactory results and outperforming a recent zero-shot TTS engine by a large margin.
Given a piece of speech and its transcript text, text-based speech editing aims to generate speech that can be seamlessly inserted into the given speech by editing the transcript. Existing methods adopt a two-stage approach: synthesize the input text using a generic text-to-speech (TTS) engine and then transform the voice to the desired voice using voice conversion (VC). A major problem of this framework is that VC is a challenging problem which usually needs a moderate amount of parallel training data to work satisfactorily. In this paper, we propose a one-stage context-aware framework to generate natural and coherent target speech without any training data of the target speaker. In particular, we manage to perform accurate zero-shot duration prediction for the inserted text. The predicted duration is used to regulate both text embedding and speech embedding. Then, based on the aligned cross-modality input, we directly generate the mel-spectrogram of the edited speech with a transformer-based decoder. Subjective listening tests show that despite the lack of training data for the speaker, our method has achieved satisfactory results. It outperforms a recent zero-shot TTS engine by a large margin.