title: HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive  Models
abstract: This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive
  models for raw audio waveform generation. As an example, we propose
  a hybrid model that combines an autoregressive network named WaveNet and a
  conventional LSTM model to address speech synthesis. Instead of generating
  one sample per time-step, the proposed HybridNet generates multiple samples per
  time-step by exploiting the long-term memory utilization property of LSTMs. In
  the evaluation, when applied to text-to-speech, HybridNet yields state-of-art performance.
  HybridNet achieves a 3.83 subjective 5-scale mean opinion score on
  US English, largely outperforming the same size WaveNet in terms of naturalness
  and provide 2x speed up at inference.
authors: Yanqi Zhou Wei Ping Sercan Arik Kainan Peng Greg Diamos
hIndex: Greg Diamos 28