The blizzard machine learning challenge 2017

Abstract

This paper describes the Blizzard Machine Learning Challenge (BMLC) 2017, which is a spin-off of the Blizzard Challenge. The annual Blizzard Challenges 2005–2017 were held to better understand and compare research techniques in building corpus-based text-to-speech (TTS) systems on the same data. The series of Blizzard Challenges has helped us measure progress in TTS technology. However, to get competitive performance, a lot time has to be spent on skilled tasks. This may make the Blizzard Challenge unattractive to machine learning researchers from other fields. Therefore, we recommend that the BMLC not involve these speech-specific tasks and that it allow participants to concentrate on the acoustic modeling task, framed as a straightforward machine learning problem, with a fixed dataset. In the BMLC 2017, two types of datasets consisting of four hours of speech data suitable for machine learning problems were distributed. This paper summarizes the purpose, design, and whole process of the challenge and its results.