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A simplified speech recognition system that uses the maximum mutualinformation (MMI) criterion is considered. End-to-end training using gradientdescent is suggested, similarly to the training of connectionist temporalclassification (CTC). We use an MMI criterion with a simple language model inthe training stage, and a standard HMM decoder. Our method compares favorablyto CTC in terms of performance, robustness, decoding time, disk footprint andquality of alignments. The good alignments enable the use of a straightforwardensemble method, obtained by simply averaging the predictions of several neuralnetwork models, that were trained separately end-to-end. The ensemble methodyields a considerable reduction in the word error rate.

Simplified End-to-End MMI Training and Voting for ASR