Phone Based Keyword Spotting for Transcribing Very Low Resource Languages

Eric Le Ferrand, Steven Bird, laurent besacier

Abstract:

We investigate the efficiency of two very different spoken term detection approaches for transcription when the available data is insufficient
to train a robust speech recognition system.
This work is grounded in a very low-resource
language documentation scenario where only
a few minutes of recording have been transcribed for a given language so far. Experiments on two oral languages show that a pretrained universal phone recognizer, fine-tuned
with only a few minutes of target language
speech, can be used for spoken term detection through searches in phone confusion networks with a lexicon expressed as a finite
state automaton. Experimental results show
that a phone recognition based approach provides better overall performances than Dynamic Time Warping when working with clean
data, and highlight the benefits of each methods for two types of speech corpus.