Multimodal speech synthesis architecture for unsupervised speaker adaptation

TL;DR

This paper introduces a novel multimodal neural speech synthesis architecture that enables unsupervised speaker adaptation with minimal data, improving multi-speaker modeling and adaptation without transcriptions.

Contribution

The paper presents a new architecture and training schemes for unsupervised speaker adaptation in neural speech synthesis, applicable to both transcribed and untranscribed data.

Findings

Enables adaptation to unseen speakers using untranscribed speech.

Improves multi-speaker modeling performance.

Effective with both transcribed and untranscribed audio data.

Abstract

This paper proposes a new architecture for speaker adaptation of multi-speaker neural-network speech synthesis systems, in which an unseen speaker's voice can be built using a relatively small amount of speech data without transcriptions. This is sometimes called "unsupervised speaker adaptation". More specifically, we concatenate the layers to the audio inputs when performing unsupervised speaker adaptation while we concatenate them to the text inputs when synthesizing speech from text. Two new training schemes for the new architecture are also proposed in this paper. These training schemes are not limited to speech synthesis, other applications are suggested. Experimental results show that the proposed model not only enables adaptation to unseen speakers using untranscribed speech but it also improves the performance of multi-speaker modeling and speaker adaptation using transcribed audio files.