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The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
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In: Interspeech 2020 - Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-02962224 ; Interspeech 2020 - Conference of the International Speech Communication Association, Oct 2020, Shangai / Virtual, China (2020)
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A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
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In: Language Resources and Evaluation Conference (LREC) ; https://hal.archives-ouvertes.fr/hal-01807093 ; Language Resources and Evaluation Conference (LREC), Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Pi, May 2018, Miyazaki, Japan (2018)
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A K-nearest neighbours approach to unsupervised spoken term discovery
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In: IEEE Spoken Language Technology SLT-2018 ; https://hal.archives-ouvertes.fr/hal-01947953 ; IEEE Spoken Language Technology SLT-2018, Dec 2018, Athènes, Greece (2018)
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Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems
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In: Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-01026368 ; Language Resources and Evaluation Conference, May 2014, Reykyavik, Iceland (2014)
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Bridging the gap between speech technology and natural language processing : an evaluation toolbox for term discovery systems
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Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems
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In: http://www.lscp.net/persons/dupoux/papers/Ludusan_VJGCJD_2014_Evaluation_toolbox_term_discovery.LREC.pdf
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Abstract:
The unsupervised discovery of linguistic terms from either continuous phoneme transcriptions or from raw speech has seen an increasing interest in the past years both from a theoretical and a practical standpoint. Yet, there exists no common accepted evaluation method for the systems performing term discovery. Here, we propose such an evaluation toolbox, drawing ideas from both speech technology and natural language processing. We first transform the speech-based output into a symbolic representation and compute five types of evaluation metrics on this representation: the quality of acoustic matching, the quality of the clusters found, and the quality of the alignment with real words (type, token, and boundary scores). We tested our approach on two term discovery systems taking speech as input, and one using symbolic input. The latter was run using both the gold transcription and a transcription obtained from an automatic speech recognizer, in order to simulate the case when only imperfect symbolic information is available. The results obtained are analysed through the use of the proposed evaluation metrics and the implications of these metrics are discussed.
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Keyword:
evaluation; spoken term discovery; word segmentation
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.639.4211
http://www.lscp.net/persons/dupoux/papers/Ludusan_VJGCJD_2014_Evaluation_toolbox_term_discovery.LREC.pdf
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