Statistical Parametric Speech Synthesis Using Bottleneck Representation From Sequence Auto-encoder
This work addresses computational bottlenecks in speech synthesis for applications requiring efficient processing, though it is incremental as it builds on existing deep neural network techniques.
The paper tackles the computational inefficiency of frame-level acoustic representation in statistical parametric speech synthesis by proposing a unit-level representation using a recurrent neural network auto-encoder, which synthesizes speech at the same quality as conventional methods but with highly reduced computational cost.
In this paper, we describe a statistical parametric speech synthesis approach with unit-level acoustic representation. In conventional deep neural network based speech synthesis, the input text features are repeated for the entire duration of phoneme for mapping text and speech parameters. This mapping is learnt at the frame-level which is the de-facto acoustic representation. However much of this computational requirement can be drastically reduced if every unit can be represented with a fixed-dimensional representation. Using recurrent neural network based auto-encoder, we show that it is indeed possible to map units of varying duration to a single vector. We then use this acoustic representation at unit-level to synthesize speech using deep neural network based statistical parametric speech synthesis technique. Results show that the proposed approach is able to synthesize at the same quality as the conventional frame based approach at a highly reduced computational cost.