The goal of this shared task is to benchmark and promote speech translation technology for a diverse range of dialects and low-resource languages. While significant research progress has been demonstrated recently on popular datasets, many of the world’s dialects and low-resource languages lack the parallel data at scale needed for standard supervised learning. We will likely require creative approaches in leveraging disparate resources.

For example, to translate dialectal speech such as Tunisian Arabic, one may leverage existing speech and text resources in Modern Standard Arabic. Or, to translate a low-resource language such as Tamasheq, one may need to leverage word-level translation resources and raw audio.

We will provide training and evaluation data for 8 typologically diverse language-pairs. Participants are free to participate in any number of language-pairs in this track, but we highly encourage participation in as many as possible. We welcome both dedicated systems that are designed to a single language-pair, as well as general recipes aimed at improving speech translation broadly for a wide typology of languages.

General Information for All Language-Pairs

The submission format will be standardized across all language-pairs. Participants can submit systems under two conditions:

  • Constrained condition: systems are trained only on the datasets provided by the organizers (listed below)
  • Unconstrained condition: systems can be trained with any resource, including pre-trained models. Multilingual models are allowed.

Information about data and baselines are provided in the sections specific to each language pair.

Data and Baselines

Dialectal Arabic to English (ara-eng)

This language pair will focus on evaluating performance on three Arabic vernaculars:

  • Tunisian (ISO-3 code: aeb)
  • Moroccan (ISO-3 code: ary)
  • North Levantine (ISO-3 code: apc)

We point the participants to training data across different Arabic varieties:

  • The aeb-eng training data are the same as the one used in the IWSLT 2022 and 2023 tracks: We suggest you follow the train/dev/test1 split instructions according to the linked webpage.

IWSLT participants may obtain the Tunisian-English speech translation data for no cost from LDC. Please sign this form and email it to This 3-way parallel data corresponds to 160 hours and 200k lines worth of aligned audio in Tunisian speech, Tunisian transcripts, and English translations. All datasets have been manually segmented at the utterance level.

We also provide links to speech recognition datasets that include Arabic data:

Bemba to English (bem-eng)

Bemba is a Bantu language, spoken by over 10 million people in Zambia and other parts of Africa.

Data are based on the corpus described in this paper, providing 180 hours of Bemba speech, along with transcriptions and translations in English. They are available for download in this Github link.

Additional Bemba speech data (with transcriptions) are available here:

Bhojpuri to Hindi (bho-hin)

Details coming soon.

Irish to English (gle-eng)

Irish (also known as Gaeilge) has around 170,000 L1 speakers and “1.85 million (37%) people across the island (of Ireland) claim to be at least somewhat proficient with the language”. In the Republic of Ireland, it is the national and first official language. It is also one of the official languages of the European Union and a recognized minority language in Northern Ireland.

IWSLT participants may obtain the Irish-English speech translation data from here. Please sign this form to get acess credentials. This corpus consists of 11 hours of audio speech data and translations into English text.

Maltese to English (mlt-eng)

Please fill out this form to request the Maltese-English data for the LowResSLT task. The data is divided into three parts, and we are releasing around 2.5 hours of audio with Maltese transcription and English translation. We are also releasing about 7.5 hours of audio with only Maltese transcription. We will also point to some parallel text corpora for Maltese-English translation.

After filling out this form, you will get the link to download the data as well as an email from us with the link for the dataset, if we update something for the same.

Marathi to Hindi (mar-hin)

Marathi is an Indo-Aryan language dominantly spoken in India’s Maharashtra state. It is one of the 22 scheduled languages of India and the official language of Maharashtra and Goa. As per the 2011 Census of India, it has around 83 million speakers which covers 6.86% of the country’s total population. Marathi speakers rank third amongst the languages that are spoken in India.

IWSLT participants may obtain the Marathi-Hindi speech translation data without any cost. Please sign this form and email it to This corpus consists of 30 hours of audio speech data from the news domain and translations into Hindi text.

We point participants to additional Marathi audio data (with transcriptions) from here:

Quechua to Spanish (que-spa)

Quechua is an indigenous language spoken by more than 8 million people in South America. It is mainly spoken in Peru, Ecuador, and Bolivia where the official high-resource language is Spanish. It is a highly inflective language based on its suffixes which agglutinate and found to be similar to other languages like Finnish. The average number of morphemes per word (synthesis) is about two times larger than English. English typically has around 1.5 morphemes per word and Quechua has about 3 morphemes per word.

There are two main region divisions of Quechua known as Quechua I and Quechua II. This data set consists of two main types of Quechua spoken in Ayacucho, Peru (Quechua Chanka ISO:quy) and Cusco, Peru (Quechua Collao ISO:quz) which are both part of Quechua II and, thus, considered “southern” languages. We label the data set with que - the ISO code for Quechua II mixtures.

IWSLT participants may obtain the public Quechua-Spanish speech translation dataset along with the additonal parallel (text-only) data for the constrained task at no cost here: IWSLT 2023 QUE-SPA Data set. IWSLT particpants should also feel free to use any publicly available data for the unconstrained task. This includes a data set of 60 hours of fully transcribed Quechua audio which can be obtained by emailing and

Tamasheq to French (tmh-fra)

Tamasheq is a variety of Tuareg, a Berber macro-language spoken by nomadic tribes across North Africa in Algeria, Mali, Niger and Burkina Faso. It accounts for approximately 500,000 native speakers, being mostly spoken in Mali and Niger. This task is about translating spoken Tamasheq into written French. Almost 20 hours of spoken Tamasheq with French translation are freely provided by the organizers. A major challenge is that no Tamasheq transcription is provided.

  • Speech-to-translation parallel data: here
  • Additional audio data (see description in the above Github page): here
  • The corpus is described in this paper


We provide various baselines:

  • For Arabic, feel free to build upon the baseline models in ESPnet provided by CMU WAVLab. Here are the recipes for the basic condition: ASR model and ST model. You may also find it helpful to refer to the system description papers in 2022 from CMU, JHU, and ON-TRAC, or the 2022 findings paper.

  • A baseline system for Tamasheq is available as a SpeechBrain recipe here. This system is the one which got the best result during the IWSLT22 edition with a BLEU score of 5.7.

  • We also direct the participants to the IWSLT 2023 findings paper for best practices based on last year’s shared task. Papers describing last year’s submitted systems are listed here.


Participants will submit their final predictions in the following format for all language pairs.

We will primarily focus on speech translation results (“st”), but participants are welcome to share intermediate speech recognition outputs as well (“asr”).

We ask participants to identify their primary submission (which will be used for the final ranking). We will also allow up to two contrastive submissions (“contrastive1”, “contrastive2”).

Please name all files as follows:

  • [team_name].[task].[type].[label].[language-pair].txt


  • “team_name” is the name of the team
  • “task” is one of “st” and “asr”
  • “type” is one of “constrained” and “unconstrained”
  • “label” is one of “primary”, “contrastive1”, or “contrastive2”
  • “language-pair” uses the three-letter ISO codes defined above (e.g. que-spa for Quechua to Spanish)

If participants do not have a constrained/unconstrained system or primary, constrastive1, constrastive2 they should submit only the files that they have, please do NOT repeat submissions.

Submission files should contain translations (or transcriptions) in the format of one per line following the format of the segments file (in sequence) corresponding to the test data splits.

Submission details will be provided with the release of the test data.


The official BLEU score will use lower-case and no punctuation, following the “norm” files in the setup instructions.

We will also aim for a human evaluation of the translation outputs.



  • Kenton Murray, Johns Hopkins University
  • Mateusz Krubiński, Institute of Formal and Applied Linguistics, Charles University (krubinski [email symbol]
  • Pavel Pecina, Institute of Formal and Applied Linguistics, Charles University (pecina [email symbol]


  • Antonios Anastasopoulos, George Mason University (antonis [email symbol]
  • Claytone Sikasote, University of Zambia (claytone.sikasote [email symbol]

Bhojpuri, Irish, Marathi:

  • Atul Kr. Ojha - University of Galway (atulkumar.ojha [email symbol]


  • Claudia Borg, University of Malta (claudia.borg [email symbol]
  • Rishu Kumar, Charles University (kumarri [email symbol]>


  • Rodolfo Zevallos - Universitat Pompeu Fabra (rodolfojoel.zevallos [email symbol]
  • William Chen - Carnegie Mellon Univerisy (wc4 [email symbol]
  • Ibrahim Ahmed - Northeastern University (i.ahmad [email symbol]
  • John Ortega - Northeastern University (j.ortega [email symbol]


  • Yannick Estève - Avignon University (yannick.esteve [email symbol]


Chair: Antonios Anastasopoulos, George Mason University


Please use the tag [LowRes] in your email title when emailing the above googlegroup.