Unsupervised training of subspace gaussian mixture models for conversational telephone speech recognition

This paper presents our preliminary works on exploring unsupervised training of subspace gaussian mixture models for under-resourced CTS recognition task. The subspace model yields better performance than conventional GMM model, particularly in small or middle-sized training set. As an effective way to save human efforts, unsupervised learning is often applied to automatically transcribe a large amount of speech archives. The additional auto-transcribed data may help to improve model accuracy. In this paper, experiments are carried out on two publicly available English conversational telephone speech corpora. Both GMM and SGMM model in combination with unsupervised learning are examined and compared in this paper.