Bogdan Ludusan


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The Role of Prosody and Speech Register in Word Segmentation: A Computational Modelling Perspective
Bogdan Ludusan | Reiko Mazuka | Mathieu Bernard | Alejandrina Cristia | Emmanuel Dupoux
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This study explores the role of speech register and prosody for the task of word segmentation. Since these two factors are thought to play an important role in early language acquisition, we aim to quantify their contribution for this task. We study a Japanese corpus containing both infant- and adult-directed speech and we apply four different word segmentation models, with and without knowledge of prosodic boundaries. The results showed that the difference between registers is smaller than previously reported and that prosodic boundary information helps more adult- than infant-directed speech.


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Motif discovery in infant- and adult-directed speech
Bogdan Ludusan | Amanda Seidl | Emmanuel Dupoux | Alex Cristia
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning

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Prosodic boundary information helps unsupervised word segmentation
Bogdan Ludusan | Gabriel Synnaeve | Emmanuel Dupoux
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems
Bogdan Ludusan | Maarten Versteegh | Aren Jansen | Guillaume Gravier | Xuan-Nga Cao | Mark Johnson | Emmanuel Dupoux
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

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.