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arXiv · 2204.08858

An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition

Niko Moritz·Frank Seide·Duc Le·Jay Mahadeokar·Christian Fuegen
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Abstract

The two most popular loss functions for streaming end-to-end automatic speech recognition (ASR) are RNN-Transducer (RNN-T) and connectionist temporal classification (CTC). Between these two loss types we can classify the monotonic RNN-T (MonoRNN-T) and the recently proposed CTC-like Transducer (CTC-T). Monotonic transducers have a few advantages. First, RNN-T can suffer from runaway hallucination, where a model keeps emitting non-blank symbols without advancing in time. Secondly, monotonic transducers consume exactly one model score per time step and are therefore more compatible with traditional FST-based ASR decoders. However, the MonoRNN-T so far has been found to have worse accuracy than RNN-T. It does not have to be that way: By regularizing the training via joint LAS training or parameter initialization from RNN-T, both MonoRNN-T and CTC-T perform as well or better than RNN-T. This is demonstrated for LibriSpeech and for a large-scale in-house data set.

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Niko Moritz, Frank Seide, Duc Le, Jay Mahadeokar, Christian Fuegen. 2022-04-19. An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition. https://arxiv.org/abs/2204.08858

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