Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations

Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using interpolated features with scaling parameters in emotional latent space. However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc. In this paper, we propose a novel method to control the continuous intensity of emotions using semi-supervised learning. The model learns emotions of intermediate intensity using pseudo-labels generated from phoneme-level sequences of speech information. An embedding space built from the proposed model satisfies the uniform grid geometry with an emotional basis. The experimental results showed that the proposed method was superior in controllability and naturalness.Comment: Accepted by Interspeech 202

Comparing acoustic and textual representations of previous linguistic context for improving Text-to-Speech

Text alone does not contain sufficient information to predict the spoken form. Using additional information, such as the linguistic context, should improve Text-to-Speech naturalness in general, and prosody in particular. Most recent research on using context is limited to using textual features of adjacent utterances, extracted with large pre-trained language models such as BERT. In this paper, we compare multiple representations of linguistic context by conditioning a Text-to-Speech model on features of the preceding utterance. We experiment with three design choices: (1) acoustic vs. textual representations; (2) features extracted with large pre-trained models vs. features learnt jointly during training; and (3) representing context at the utterance level vs. word level. Our results show that appropriate representations of either text or acoustic context alone yield significantly better naturalness than a baseline that does not use context. Combining an utterance-level acoustic representation with a word-level textual representation gave the best results overall