@inproceedings{ferrand-etal-2021-phone,
    title = "Phone Based Keyword Spotting for Transcribing Very Low Resource Languages",
    author = "Ferrand, Eric Le  and
      Bird, Steven  and
      Besacier, Laurent",
    editor = "Rahimi, Afshin  and
      Lane, William  and
      Zuccon, Guido",
    booktitle = "Proceedings of the 19th Annual Workshop of the Australasian Language Technology Association",
    month = dec,
    year = "2021",
    address = "Online",
    publisher = "Australasian Language Technology Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.alta-1.8/",
    pages = "79--86",
    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."
}