Lightly supervised learning from a damaged natural speech corpus

Abstract

Large corpora of transcribed speech are rare and expensive to acquire, but valuable for ASR systems. Of current research interest are corpora of natural speech, i.e. far-field recordings of multiple speakers in noisy environments. In the big data era there are many speech transcriptions collected for purposes other than ASR, which omit features required by typical ASR systems such as timing information. If we could recover training data from such `found' corpora this would open up large new resources for ASR research. We present a case study for this type of data recovery - becoming known as `lightly supervised learning' - for a highly damaged corpus called Family Life. We use a novel comparison of a parallel decode and forced audio alignment to iteratively select and grow good data. Family Life also has unusual data mislabelling problems which can be addressed by an integrated tfidf approach. These methods reduce WER on the corpus from 83.0 to 57.2. We also discuss a probabilistic loose string alignment approach which removes untranscribed `icebreaker' speech.