12th Annual Conference of the International Speech Communication Association

Florence, Italy
August 27-31. 2011

Hidden Boosted MMI and Hierarchical State Posterior Feature for Automatic Speech Recognition Based on Hidden Conditional Neural Fields

Yasuhisa Fujii, Kazumasa Yamamoto, Seiichi Nakagawa

Toyohashi University of Technology, Japan

We have investigated automatic speech recognition using Hidden Conditional Neural Fields (HCNF). In this paper, we propose a new objective function, Hidden Boosted MMI (HB-MMI) that considers the number of errors in the training data even if the correct state sequence is not known for training the HCNF. The experimental results show that HB-MMI can improve recognition accuracy if overfitting does not occur. We also present an automatic speech recognition method using a hierarchical state posterior feature where the output from the first stage HCNF is used as input for the second stage HCNF. The experimental results show that the feature improves recognition accuracy. By combining both of the proposed methods, we obtain further improvements.

Full Paper

Bibliographic reference.  Fujii, Yasuhisa / Yamamoto, Kazumasa / Nakagawa, Seiichi (2011): "Hidden boosted MMI and hierarchical state posterior feature for automatic speech recognition based on hidden conditional neural fields", In INTERSPEECH-2011, 1001-1004.