The zero resource speech challenge 2015

Abstract

The Interspeech 2015 Zero Resource Speech Challenge aims at discovering subword and word units from raw speech. The challenge provides the first unified and open source suite of evaluation metrics and data sets to compare and analyse the results of unsupervised linguistic unit discovery algorithms. It consists of two tracks. In the first, a psychophysically inspired evaluation task (minimal pair ABX discrimination) is used to assess how well speech feature representations discriminate between contrastive subword units. In the second, several metrics gauge the quality of discovered word-like patterns. Two data sets are provided, one for English, one for Xitsonga. Both data sets are provided without any annotation except for voice activity and talker identity. This paper introduces the evaluation metrics, presents the results of baseline systems and discusses some of the key issues in unsupervised unit discovery. Index Terms: zero resource speech challenge, feature extraction, unsupervised term discovery, new paradigms