Boosting HMM acoustic models in large vocabulary speech recognition
Boosting algorithms have been successfully used to improve performance in a variety of classification tasks. Here, we suggest an approach to apply a popular boosting algorithm (called AdaBoost.M2) to Hidden Markov Model based speech recognizers, at the level of utterances. In a variety of recognition tasks we show that boosting significantly improves the best test error rates obtained with standard maximum likelihood training. In addition, results in several isolated word decoding experiments show that boosting may also provide further performance gains over discriminative training, when both training techniques are combined. In our experiments this also holds when comparing final classifiers with a similar number of parameters and when evaluating in decoding conditions with lexical and acoustic mismatch to the training conditions. Moreover, we present an extension of our algorithm to large vocabulary continuous speech recognition, allowing online recognition without further processing of N-best lists or word lattices. This is achieved by using a lexical approach for combining different acoustic models in decoding. In particular, we introduce a weighted summation over an extended set of alternative pronunciation models representing both the boosted models and the baseline model. In this way, arbitrarily long utterances can be recognized by the boosted ensemble in a single pass decoding framework. Evaluation results are presented on two tasks: a real-life spontaneous speech dictation task with a 60k word vocabulary and Switchboard.
boosting adaboost spontaneous speech machine learning acoustic model training automatic speech recognition