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Investigating alignment interpretability for low-resource NMT
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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Is there a bilingual disadvantage for word segmentation? A computational modeling approach
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In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021)
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SM to: Is there a bilingual disadvantage for word segmentation? A computational modeling approach ...
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Early Tashelhiyt Berber word segmentation: the role of the Possible Word Constraint ...
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Discovering structure in speech recordings: Unsupervised learning of word and phoneme like units for automatic speech recognition
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In: Fraunhofer IAIS (2021)
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Handling cross and out-of-domain samples in Thai word segmentation
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In: 1003 ; 1016 (2021)
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Measuring (online) word segmentation in adults and children
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In: Dutch Journal of Applied Linguistics, Vol 10 (2021) (2021)
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Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
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In: Proceedings of the 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020), ; SLTU-CCURL workshop, LREC 2020 ; https://hal.archives-ouvertes.fr/hal-02895907 ; SLTU-CCURL workshop, LREC 2020, May 2020, Marseille, France (2020)
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F0 Slope and Mean: Cues to Speech Segmentation in French
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In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-03042331 ; Interspeech 2020, Oct 2020, Shanghai, China. pp.1610-1614, ⟨10.21437/Interspeech.2020-2509⟩ (2020)
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| 11 |
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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Data for: The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
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Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
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Soderstrom, M; Karadayi, J; Casillas, M; Riebling, E; Räsänen, O; Cristia, A; Metze, F; Seshadri, S; Rosemberg, C; Bunce, J; Bergelson, E. - : Elsevier BV, 2020
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Abstract:
© 2019 The Authors Automatic word count estimation (WCE) from audio recordings can be used to quantify the amount of verbal communication in a recording environment. One key application of WCE is to measure language input heard by infants and toddlers in their natural environments, as captured by daylong recordings from microphones worn by the infants. Although WCE is nearly trivial for high-quality signals in high-resource languages, daylong recordings are substantially more challenging due to the unconstrained acoustic environments and the presence of near- and far-field speech. Moreover, many use cases of interest involve languages for which reliable ASR systems or even well-defined lexicons are not available. A good WCE system should also perform similarly for low- and high-resource languages in order to enable unbiased comparisons across different cultures and environments. Unfortunately, the current state-of-the-art solution, the LENA system, is based on proprietary software and has only been optimized for American English, limiting its applicability. In this paper, we build on existing work on WCE and present the steps we have taken towards a freely available system for WCE that can be adapted to different languages or dialects with a limited amount of orthographically transcribed speech data. Our system is based on language-independent syllabification of speech, followed by a language-dependent mapping from syllable counts (and a number of other acoustic features) to the corresponding word count estimates. We evaluate our system on samples from daylong infant recordings from six different corpora consisting of several languages and socioeconomic environments, all manually annotated with the same protocol to allow direct comparison. We compare a number of alternative techniques for the two key components in our system: speech activity detection and automatic syllabification of speech. As a result, we show that our system can reach relatively consistent WCE accuracy across multiple corpora and languages (with some limitations). In addition, the system outperforms LENA on three of the four corpora consisting of different varieties of English. We also demonstrate how an automatic neural network-based syllabifier, when trained on multiple languages, generalizes well to novel languages beyond the training data, outperforming two previously proposed unsupervised syllabifiers as a feature extractor for WCE.
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Keyword:
Acoustics; Automatic syllabification; Computer Science; Daylong recordings; Interdisciplinary Applications; Language acquisition; LENA(TM); Noise robustness; RELIABILITY; Science & Technology; SEGMENTATION; SYSTEM; Technology; Word count estimation
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URL: https://hdl.handle.net/10161/19710
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Infants Segment Words from Songs—An EEG Study
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In: Brain Sciences ; Volume 10 ; Issue 1 (2020)
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Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning
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Controlling Utterance Length in NMT-based Word Segmentation with Attention
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-02343206 ; International Workshop on Spoken Language Translation, Nov 2019, Hong-Kong, China (2019)
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Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus
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In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-02274050 ; Open Mind, MIT Press, 2019, 3, pp.13-22. ⟨10.1162/opmi_a_00022⟩ (2019)
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Unsupervised word discovery for computational language documentation ; Découverte non-supervisée de mots pour outiller la linguistique de terrain
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In: https://tel.archives-ouvertes.fr/tel-02286425 ; Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2019. English. ⟨NNT : 2019SACLS062⟩ (2019)
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MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language
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In: Information ; Volume 10 ; Issue 10 (2019)
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