Cross-speaker emotion disentangling and transfer for end-to-end speech synthesis

Abstract

The cross-speaker emotion transfer task in text-tospeech (TTS) synthesis particularly aims to synthesize speech for a target speaker with the emotion transferred from reference speech recorded by another (source) speaker. During the emotion transfer process, the identity information of the source speaker could also affect the synthesized results, resulting in the issue of speaker leakage, i.e., synthetic speech may have the voice identity of the source speaker rather than the target speaker. This paper proposes a new method with the aim to synthesize controllable emotional expressive speech and meanwhile maintain the target speaker's identity in the cross-speaker emotion TTS task. The proposed method is a Tacotron2-based framework with the emotion embedding as the conditioning variable to provide emotion information. Two emotion disentangling modules are contained in our method to 1) get speaker-independent and emotion-discriminative embedding, and 2) explicitly constrain the emotion and speaker identity of synthetic speech to be that as expected. Moreover, we present an intuitive method to control the emotional strength in the synthetic speech for the target speaker. Specifically, the learned emotion embedding is adjusted with a flexible scalar value, which allows controlling the emotion strength conveyed by the embedding. Extensive experiments have been conducted on a Mandarin disjoint corpus, and the results demonstrate that the proposed method is able to synthesize reasonable emotional speech for the target speaker. Compared to the state-of-the-art reference embedding learned methods, our method gets the best performance on the cross-speaker emotion transfer task, indicating that our method achieves the new state-of-the-art performance on learning the speaker-independent emotion embedding. Furthermore, the strength ranking test and pitch trajectories plots demonstrate that the proposed method can effectively control the emotion strength, leading to prosodydiverse synthetic speech.

Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis

“…Generally, with a well-trained neural acoustic model [2,5,6,7] and a neural vocoder [8,9,10,11], or alternatively using fully end-to-end models [12,13,14] which directly construct wave signals from text input, it is able to synthesize high-quality neutral speech. Recently, much attention has been attracted to synthesizing expressive speech, such as stylized speech [15,16], emotional speech [17,18,19,20,21,22], and also singing voice [23,24].…”