Outlier detection for acoustic model training using robust statistics

In this paper, we propose an acoustic model training technique which is robust against outliers such as clipping, unexpected noise, poorly pronounced word segments, or mistranscriptions, which deteriorate the quality of the acoustic models and in turn decrease speech recognition performance. The outlier-robust acoustic model training technique is based on a maximum likelihood (ML) criterion and automatically detects and removes outliers from the training data. Experiments with artificially contaminated mis-transcribed training data show that nearly the same word error rate can be obtained for contaminated data using the proposed technique as for uncontaminated data. Application to a dialogue speech database with unknown outliers reduces the errors by 4.03%.