Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR
arXiv:1806.10306 · doi:10.21437/Interspeech.2018-1021
Abstract
In automatic speech recognition (ASR) systems, recurrent neural network language models (RNNLM) are used to rescore a word lattice or N-best hypotheses list. Due to the expensive training, the RNNLM's vocabulary set accommodates only small shortlist of most frequent words. This leads to suboptimal performance if an input speech contains many out-of-shortlist (OOS) words. An effective solution is to increase the shortlist size and retrain the entire network which is highly inefficient. Therefore, we propose an efficient method to expand the shortlist set of a pretrained RNNLM without incurring expensive retraining and using additional training data. Our method exploits the structure of RNNLM which can be decoupled into three parts: input projection layer, middle layers, and output projection layer. Specifically, our method expands the word embedding matrices in projection layers and keeps the middle layers unchanged. In this approach, the functionality of the pretrained RNNLM will be correctly maintained as long as OOS words are properly modeled in two embedding spaces. We propose to model the OOS words by borrowing linguistic knowledge from appropriate in-shortlist words. Additionally, we propose to generate the list of OOS words to expand vocabulary in unsupervised manner by automatically extracting them from ASR output.
5 pages, 1 figure, accepted at INTERSPEECH 2018
References in corpus (4)
Cited by in corpus (4)
- Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
- On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
- Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
- Enriching Under-Represented Named-Entities To Improve Speech Recognition Performance