<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ferrand-etal-2021-phone">
    <titleInfo>
        <title>Phone Based Keyword Spotting for Transcribing Very Low Resource Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Eric</namePart>
        <namePart type="given">Le</namePart>
        <namePart type="family">Ferrand</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Steven</namePart>
        <namePart type="family">Bird</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Laurent</namePart>
        <namePart type="family">Besacier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association</title>
        </titleInfo>
        <originInfo>
            <publisher>Australasian Language Technology Association</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">ferrand-etal-2021-phone</identifier>
    <location>
        <url>https://aclanthology.org/2021.alta-1.8</url>
    </location>
    <part>
        <date>2021-12</date>
        <extent unit="page">
            <start>79</start>
            <end>86</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Phone Based Keyword Spotting for Transcribing Very Low Resource Languages
%A Ferrand, Eric Le
%A Bird, Steven
%A Besacier, Laurent
%S Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association
%D 2021
%8 December
%I Australasian Language Technology Association
%C Online
%F ferrand-etal-2021-phone
%X 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.
%U https://aclanthology.org/2021.alta-1.8
%P 79-86

Markdown (Informal)

[Phone Based Keyword Spotting for Transcribing Very Low Resource Languages](https://aclanthology.org/2021.alta-1.8) (Ferrand et al., ALTA 2021)

• Phone Based Keyword Spotting for Transcribing Very Low Resource Languages (Ferrand et al., ALTA 2021)

ACL

• Eric Le Ferrand, Steven Bird, and Laurent Besacier. 2021. Phone Based Keyword Spotting for Transcribing Very Low Resource Languages. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, pages 79–86, Online. Australasian Language Technology Association.