Median-based generation of synthetic speech durations using a non-parametric approach

By Srikanth Ronanki, Oliver Watts, Simon King, Gustav Eje Henter. 2016

19 citations recorded by the source corpus.

This paper proposes a new approach to duration modelling for statistical parametric speech synthesis in which a recurrent statistical model is trained to output a phone transition probability at each timestep (acoustic frame). Unlike conventional approaches to duration modelling - which assume that duration distributions have a particular form (e.g., a Gaussian) and use the mean of that distribution for synthesis - our approach can in principle model any distribution supported on the non-negative integers. Generation from this model can be performed in many ways; here we consider output generation based on the median predicted duration. The median is more typical (more probable) than the conventional mean duration, is robust to training-data irregularities, and enables incremental generation. Furthermore, a frame-level approach to duration prediction is consistent with a longer-term goal of modelling durations and acoustic features together. Results indicate that the proposed method is competitive with baseline approaches in approximating the median duration of held-out natural speech.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Deep Voice 2: Multi-Speaker Neural Text-to-Speech (2017, methodology): “…deep learning has been used for a variety of subcomponents, including duration prediction (Zen et al., 2016), fundamental frequency prediction (Ronanki et al., 2016), acoustic modeling (Zen and Sak, 2015), and more recently autoregressive sample-by-sample audio waveform generation (e.g., Oord…”