Investigating lightly supervised acoustic model training

Abstract

The last decade has witnessed substantial progress in speech recognition technology, with todays state-of-the-art systems being able to transcribe broadcast audio data with a word error of about 20%. However, acoustic model development for the recognizers requires large corpora of manually transcribed training data. Obtaining such data is both time-consuming and expensive, requiring trained human annotators with substantial amounts of supervision. We describe some experiments using different levels of supervision for acoustic model training in order to reduce the system development cost. The experiments have been carried out using the DARPA TDT-2 corpus (also used in the SDR99 and SDR00 evaluations). Our experiments demonstrate that light supervision is sufficient for acoustic model development, drastically reducing the development cost.