Techniques for Vocabulary Expansion in Hybrid Speech Recognition Systems
This work addresses vocabulary limitations in speech recognition systems, which is an incremental improvement for users needing to recognize new words.
The paper tackles the problem of out-of-vocabulary words in hybrid speech recognition systems by exploring existing methods and presenting novel vocabulary expansion techniques to address internal subroutine issues in recognition graph processing, though no concrete results or numbers are provided.
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 non-existing 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.