Speech Resources in the Tamasheq Language

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 2066–2071 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 2066 Speech Resources in the Tamasheq Language Marcely Zanon Boito¹, Fethi Bougares², Florentin Barbier³, Souhir Gahbiche³, Lo¨ıc Barrault², Mickael Rouvier¹, Yannick Est`eve¹ ¹LIA - Avignon University, France ²LIUM - Le Mans University, France ³Airbus - France contact: {marcely.zanon-boito, yannick.esteve}@univ-avignon.fr Abstract In this paper we present two datasets for Tamasheq, a developing language mainly spoken in Mali and Niger. These two datasets were made available for the IWSLT 2022 low-resource speech translation track, and they consist of collections of radio recordings from daily broadcast news in Niger (Studio Kalangou) and Mali (Studio Tamani). We share (i) a massive amount of unlabeled audio data (671 hours) in five languages: French from Niger, Fulfulde, Hausa, Tamasheq and Zarma, and (ii) a smaller 17 hours parallel corpus of audio recordings in Tamasheq, with utterance-level translations in the French language. All this data is shared under the Creative Commons BY-NC-ND 3.0 license. We hope these resources will inspire the speech community to develop and benchmark models using the Tamasheq language. Keywords: speech corpus, speech translation, tamasheq, zarma, hausa, fulfulde, french 1. Introduction The vast majority of speech pipelines are developed for and in high-resource languages, a small percentage of languages for which there is a large amount of anno- tated data freely available (Joshi et al., 2020). This not only limits the investigation of language impact in current pipelines, as the applied languages are usually from the same subset, but it also fails to reflect the real- world performance these approaches will have in di- verse and smaller datasets. In recent years, the IWSLT evaluation campaign1 intro- duced a low-resource speech translation track focused on developing and benchmarking translation tools for under-resourced languages. While for a vast majority of these languages, there is not enough parallel data for training large translation models, in these cases we might still have access to limited disparate resources, such as word-level translations, small parallel textual data, monolingual texts and recordings. This track of the IWSLT campaign thus focuses on leveraging these different kinds of data for building effective translation systems under realistic settings. In this paper we present the resources in the Tamasheq language we share in the context of the IWSLT 2022: low-resource speech translation track. Tamasheq is a variety of Tuareg, a Berber macro-language spoken by nomadic tribes across North Africa in Algeria, Mali, Niger and Burkina Faso (Heath, 2006). It accounts for approximately 500,000 native speakers, being mostly spoken in Mali and Niger (Ethnologue: Languages of the World, 2021). We share a large audio corpus, made of 224 hours of Tamasheq, together with 417 hours in other four languages of Niger (French, Fulfulde, Hausa and Zarma). We also share a smaller corpus of 17 hours 1https://iwslt.org/2022/low-resource of Tamasheq utterances aligned with French transla- tions. We hope that these resources will represent an interesting use-case for the speech community, allow- …

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