In this paper, we describe an improvement on probabilistic latent speaker analysis method and investigate the use of probabilistic latent speaker analysis for acoustic model training. By performing co-occurrence analysis between speaker and dominant components, speaker variation is dealt with based on different trajectories. Speech recognition experiment results show that our method, although with a general acoustic model and one-pass decoding, outperform the gender-dependent acoustic model with each gender is given for test set. Further experiment shows that the probabilistic latent speaker training method, although with no adaptation stage and no adaptation data, has outperformed the eigenMLLR adaptation method.
Bibliographic reference. Su, Dan / Wu, Xihong / Chi, Huisheng (2008): "Probabilistic latent speaker training for large vocabulary speech recognition", In INTERSPEECH-2008, 1225-1228.