Multimodal speech synthesis architecture for unsupervised speaker adaptation
This addresses the problem of speaker adaptation in speech synthesis for applications requiring voice personalization without transcriptions, but it is incremental as it builds on existing multi-speaker systems.
The paper tackles unsupervised speaker adaptation for multi-speaker neural-network speech synthesis by proposing a new architecture that builds an unseen speaker's voice using a small amount of untranscribed speech data, and it shows improvements in performance for both multi-speaker modeling and adaptation with transcribed audio.
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.