EE Seminar: Simplified End-to-End MMI Training and Voting for ASR
Speaker: Lior Fritz,
M.Sc. student under the supervision of Prof. David Burshtein
Wednesday, November 22nd, 2017 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering
Simplified End-to-End MMI Training and Voting for ASR
Abstract
A simplified speech recognition system that uses the maximum mutual information (MMI) criterion is considered. End-to-end training using gradient descent is suggested, similarly to the training of connectionist temporal classification (CTC). We use an MMI criterion with a simple language model in the training stage, and a standard HMM decoder. Our method compares favorably to CTC in terms of performance, robustness, decoding time, disk footprint and quality of alignments. The good alignments enable the use of a straightforward ensemble method, obtained by simply averaging the predictions of several neural network models, that were trained separately end-to-end. The ensemble method yields a considerable reduction in the word error rate.
