We have investigated automatic speech recognition using Hidden Conditional Neural Fields (HCNF). In this paper, we propose a new objective function, Hidden Boosted MMI (HBMMI) 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. Index Terms: hidden conditional neural fields, automatic speech recognition, hidden boosted MMI, state posterior feature
['Yasuhisa Fujii', 'Kazumasa Yamamoto', 'Seiichi Nakagawa']
Hidden Boosted MMI and Hierarchical State Posterior Feature for Automatic Speech Recognition based on Hidden Conditional Neural Fields