DurIAN: Duration Informed Attention Network For Multimodal Synthesis

TL;DR

This paper introduces DurIAN, a duration-informed attention network for multimodal synthesis that produces natural speech and facial expressions, improving efficiency and avoiding common artifacts of end-to-end systems.

Contribution

The paper presents DurIAN, a novel autoregressive model using duration-informed alignments, and a multi-band WaveRNN for faster speech generation, enhancing multimodal synthesis quality and efficiency.

Findings

DurIAN achieves speech quality comparable to state-of-the-art systems.

Multi-band WaveRNN reduces computational complexity significantly.

The system enables synchronized speech and facial expression generation.

Abstract

In this paper, we present a generic and robust multimodal synthesis system that produces highly natural speech and facial expression simultaneously. The key component of this system is the Duration Informed Attention Network (DurIAN), an autoregressive model in which the alignments between the input text and the output acoustic features are inferred from a duration model. This is different from the end-to-end attention mechanism used, and accounts for various unavoidable artifacts, in existing end-to-end speech synthesis systems such as Tacotron. Furthermore, DurIAN can be used to generate high quality facial expression which can be synchronized with generated speech with/without parallel speech and face data. To improve the efficiency of speech generation, we also propose a multi-band parallel generation strategy on top of the WaveRNN model. The proposed Multi-band WaveRNN effectively reduces the total computational complexity from 9.8 to 5.5 GFLOPS, and is able to generate audio that is 6 times faster than real time on a single CPU core. We show that DurIAN could generate highly natural speech that is on par with current state of the art end-to-end systems, while at the same time avoid word skipping/repeating errors in those systems. Finally, a simple yet effective approach for fine-grained control of expressiveness of speech and facial expression is introduced.