Apply n-best list re-ranking to acoustic model combinations of boosting training

The object function for Boosting training method in acoustic modeling aims to reduce utterance level error rate. This is different from the most commonly used performance metric in speech recognition, word error rate. This paper proposes that the combination of N-best list re-ranking and ROVER can partly address this problem. In particular, model combination is applied to re-ranked hypotheses rather than to the original top-1 hypotheses and carried on word level. Improvement of system performance is observed in our experiments. In addition, we describe and evaluate a new confidence feature that measures the correctness of frame level decoding result.

[1]  Jonathan G. Fiscus,et al.  A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER) , 1997, 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings.

[2]  Rong Zhang,et al.  Comparative study of boosting and non-boosting training for constructing ensembles of acoustic models , 2003, INTERSPEECH.

[3]  Alexander I. Rudnicky,et al.  N-best speech hypotheses reordering using linear regression , 2001, INTERSPEECH.

[4]  Holger Schwenk,et al.  Using boosting to improve a hybrid HMM/neural network speech recognizer , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[5]  Say Wei Foo,et al.  Speaker recognition using adaptively boosted decision tree classifier , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Ralf Schlüter,et al.  Using word probabilities as confidence measures , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[7]  Bhiksha Raj,et al.  A boosting approach for confidence scoring , 2001, INTERSPEECH.

[8]  Alexander I. Rudnicky,et al.  Creating natural dialogs in the carnegie mellon communicator system , 1999, EUROSPEECH.

[9]  Carsten Meyer.  Utterance-level boosting of HMM speech recognizers , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.