Why speech recognizers make errors? A robustness view

Abstract

The performance of large vocabulary speech recognizers often varies depending on the input speech and the quality of the trained models. The particular attributes that cause recognition errors are a research area that has not been well studied. This paper addresses this issue from a robustness perspective using a large amount of field data collected from natural language dialog services. In particular, we present a method for tracking time-varying or nonstationary extraneous events, such as music, background noise, etc. We show that this measure is a better predictor of recognition errors than a standard measure of stationary signal-to-noise ratio (SNR). Combining the two measures provides a data selection algorithm for detecting problematic speech.