Toward human-assisted lexical unit discovery without text resources

Abstract

This work addresses lexical unit discovery for languages without (usable) written resources. Previous work has addressed this problem using entirely unsupervised methodologies.  Our approach in contrast investigates the use of linguistic and speaker knowledge which are often available even if text resources are not.  We create a framework that benefits from such resources, not assuming orthographic representations and avoiding generation of word-level transcriptions.  We adapt a universal phone recognizer to the target language and use it to convert audio into a searchable phone string for lexical unit discovery via fuzzy sub-string matching.  Linguistic knowledge is used to constrain phone recognition output and to constrain lexical unit discovery on the phone recognizer output.

Target language speakers are used to assist a linguist in creating phonetic transcriptions for the adaptation of acoustic and language models, by respeaking more clearly a small portion of the target language audio.  We also explore robust features and feature transform through deep auto-encoders for better phone recognition performance.

The proposed approach achieves lexical unit discovery performance comparable to state-of-the-art zero-resource methods.  Since the system is built on phonetic recognition, discovered units are immediately interpretable.  They can be used to automatically populate a pronunciation lexicon and enable iterative improvement through additional feedback from target language speakers.