Investigating the usefulness of i-vectors for automatic language characterization

Autor: de Seyssel, Maureen, Wisniewski, Guillaume, Dupoux, Emmanuel, Ludusan, Bogdan
Přispěvatelé: Apprentissage machine et développement cognitif (CoML), Laboratoire de sciences cognitives et psycholinguistique (LSCP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Département d'Etudes Cognitives - ENS Paris (DEC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire de Linguistique Formelle (LLF - UMR7110), Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), École des hautes études en sciences sociales (EHESS), Meta AI, RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Universität Bielefeld = Bielefeld University, This work was performed using HPC resources from GENCI-IDRIS (Grant 20XX-AD011012315). MS work was partly funded by l’Agence de l’Innovation de D´efense.
Rok vydání: 2022
Předmět:
Zdroj: Proceedings of Speech Prosody 2022
Speech Prosody 2022-11th International Conference on Speech Prosody
Speech Prosody 2022-11th International Conference on Speech Prosody, May 2022, Lisbonne, Portugal. ⟨10.21437/speechprosody.2022-94⟩
ISSN: 2333-2042
DOI: 10.21437/speechprosody.2022-94
Popis: International audience; Work done in recent years has shown the usefulness of using automatic methods for the study of linguistic typology. However, the majority of proposed approaches come from natural language processing and require expert knowledge to predict typological information for new languages. An alternative would be to use speech-based methods that do not need extensive linguistic annotations, but considerably less work has been done in this direction. The current study aims to reduce this gap, by investigating a promising speech representation, i-vectors, which by capturing suprasegmental features of language, can be used for the automatic characterization of languages. Employing data from 24 languages, covering several linguistic families, we computed the i-vectors corresponding to each sentence and we represented the languages by their centroid i-vector. Analyzing the distance between the language centroids and phonological, inventory and syntactic distances between the same languages, we observed a significant correlation between the i-vector distance and the syntactic distance. Then, we explored in more detailed a number of syntactic features and we proposed a method for predicting the value of the most promising feature, based on the i-vector information. The obtained results, an 87% classification accuracy, are encouraging and we envision to extend this method further.
Databáze: OpenAIRE
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