Emotion-Coherent Speech Data Augmentation and Self-Supervised Contrastive Style Training for Enhancing Kids's Story Speech Synthesis

Abstract

Expressive speech synthesis requires vibrant prosody and well-timed pauses. We propose an effective strategy to augment a small dataset to train an expressive end-to-end Text-to-Speech model. We merge audios of emotionally congruent text using a text emotion recognizer, creating augmented expressive speech data. By training with two-sentence audio, our model learns natural breaks between lines. We further apply self-supervised contrastive training to improve the speaking style embedding extraction from speech. During inference, our model produces multi-sentence speech in one step, guided by the text-predicted speaking style. Evaluations showcase the effectiveness of our proposed approach when compared to a baseline model trained with consecutive two-sentence audio. Our synthesized speeches give a closer inter-sentence pause distribution to the ground truth speech. Subjective evaluations reveal our synthesized speech scored higher in naturalness and style suitability than the baseline.

Accepted at IEEE Spoken Language Technology Workshop 2024

References in corpus (4)

• FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
• Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis
• WavThruVec: Latent speech representation as intermediate features for neural speech synthesis
• ContextSpeech: Expressive and Efficient Text-to-Speech for Paragraph Reading