Application of SVM-based correctness predictions to unsupervised discriminative speaker adaptation

The effectiveness of unsupervised speaker adaptation is typically limited by errors in the estimated transcription of the adaptation data. Previous work has mitigated this negative effect by using only those sections of the adaptation data which are transcribed with relatively high confidence. In this work, phoneme correctness predictions are integrated into a discriminative unsupervised speaker adaptation procedure. Significant accuracy improvements (over the equivalent likelihood-based technique) are observed when using discriminative unsupervised speaker adaptation in combination with support vector machines to predict phoneme correctness.