8 research outputs found
External Language Model Integration for Factorized Neural Transducers
We propose an adaptation method for factorized neural transducers (FNT) with
external language models. We demonstrate that both neural and n-gram external
LMs add significantly more value when linearly interpolated with predictor
output compared to shallow fusion, thus confirming that FNT forces the
predictor to act like regular language models. Further, we propose a method to
integrate class-based n-gram language models into FNT framework resulting in
accuracy gains similar to a hybrid setup. We show average gains of 18% WERR
with lexical adaptation across various scenarios and additive gains of up to
60% WERR in one entity-rich scenario through a combination of class-based
n-gram and neural LMs
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability
Because of its streaming nature, recurrent neural network transducer (RNN-T)
is a very promising end-to-end (E2E) model that may replace the popular hybrid
model for automatic speech recognition. In this paper, we describe our recent
development of RNN-T models with reduced GPU memory consumption during
training, better initialization strategy, and advanced encoder modeling with
future lookahead. When trained with Microsoft's 65 thousand hours of anonymized
training data, the developed RNN-T model surpasses a very well trained hybrid
model with both better recognition accuracy and lower latency. We further study
how to customize RNN-T models to a new domain, which is important for deploying
E2E models to practical scenarios. By comparing several methods leveraging
text-only data in the new domain, we found that updating RNN-T's prediction and
joint networks using text-to-speech generated from domain-specific text is the
most effective.Comment: Accepted by Interspeech 202
Word-Phrase-Entity Language Models: Getting More Mileage out of N-grams
Abstract We present a modification of the traditional n-gram language modeling approach that departs from the word-level data representation and seeks to re-express the training text in terms of tokens that could be either words, common phrases or instances of one or several classes. Our iterative optimization algorithm considers alternative parses of the corpus in terms of these tokens, re-estimates token n-gram probabilities and also updates within-class distributions. In this paper, we focus on the cold start approach that only assumes availability of the word-level training corpus, as well as a number of generic class definitions. Applied to the calendar scenario in the personal assistant domain, our approach reduces word error rates by more than 13% relative to the word-only n-gram language models. Only a small fraction of these improvements can be ascribed to a larger vocabulary