Rapid Evaluation of Speech Representations for Spoken Term Discovery

Acoustic front-ends are typically developed for supervised learning tasks and are thus optimized to minimize word error rate, phone error rate, etc. However, in recent efforts to develop zero-resource speech technologies, the goal is not to use transcribed speech to train systems but instead to discover the acoustic structure of the spoken language automatically. For this new setting, we require a framework for evaluating the quality of speech representations without coupling to a particular recognition architecture. Motivated by the spoken term discovery task, we present a dynamic time warping-based framework for quantifying how well a representation can associate words of the same type spoken by different speakers. We benchmark the quality of a wide range of speech representations using multiple frame-level distance metrics and demonstrate that our performance metrics can also accurately predict phone recognition accuracies.