Techniques for Vocabulary Expansion in Hybrid Speech Recognition Systems

Abstract

The problem of out of vocabulary words (OOV) is typical for any speech recognition system, hybrid systems are usually constructed to recognize a fixed set of words and rarely can include all the words that will be encountered during exploitation of the system. One of the popular approach to cover OOVs is to use subword units rather then words. Such system can potentially recognize any previously unseen word if the word can be constructed from present subword units, but also nonexisting words can be recognized. The other popular approach is to modify HMM part of the system so that it can be easily and effectively expanded with custom set of words we want to add to the system. In this paper we explore different existing methods of this solution on both graph construction and search method levels. We also present a novel vocabulary expansion techniques which solve some common internal subroutine problems regarding recognition graph processing.

Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition

“…If the system is a conventional hybrid (HMM-DNN-based acoustic model and word-based ngram language model), the OOV problem is often solved by dynamic expanding the system's vocabulary and/or adapting the language model (e.g. Khokhlov et al, 2017;Gandhe et al, 2018;Malkovsky et al, 2020). A less common approach is to use a subword-based n-gram language model (Smit et al, 2017).…”