Probabilistic Speaker-Class based Acoustic Modeling for Large Vocabulary Continuous Speech Recognition

In this paper, a probabilistic speaker-class (PSC) based acoustic modeling method is proposed for taking into account speaker variability influence in HMM-based speech recognition systems. Firstly, within the context of speaker-class based speech recognition, an experiment is conducted to investigate the performance of speaker-class recognition based on hard-cut speaker clustering. Then, in the proposed method, through introducing the probabilistic latent speaker analysis, the speaker-class dependent acoustic models are trained based on a soft-decision speaker clustering method, and combined by the distribution of speaker-class in the decoding phase. The experiments were conducted on a 600-hour speech corpus, and showed improvement in a large vocabulary continuous speech recognition task.