QUESPA Submission for the IWSLT 2025 Dialectal and Low-resource Speech Translation Task (2025)

Abstract: This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE-SPA) track featured in the Evaluation Campaign of IWSLT 2025: dialectal and low-resource speech translation. This year, there is one main submission type supported in the campaign: unconstrained. This is our third year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year’s submission. This year we submit three total unconstrained-only systems of which our best (contrastive 2) system uses last year’s best performing pre-trained language (PLM) model for ST (without cascading) and the inclusion of additional Quechua–Collao speech transcriptions found online. Fine-tuning of Microsoft’s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 26.7 BLEU. In this article, we present the three submissions along with a detailed description of the updated machine translation system where a comparison is done between synthetic, unconstrained, and other data for fine-tuning.

## introduction in this article, we describe three systems that were submitted to the iwslt 2025 low-resource track for speech translation (st). the iwslt task is particularly challenging for low-resource languages (lrls) due to the lack of data needed to create, or even fine-tune, a pre-trained language model (plm). while many problems are solvable with apis provided by large corporations such as chat-gpt or gemini, it is still the case that for lrls, zero-to-few shot approaches are needed where corporate-level apis do not contain enough data either. here, we describe three main approaches that extend previous approaches submitted in the past three iterations of @xcite @xcite where the best score gotten for st until this publishing based on bleu @xcite for the quechua to spanish task was: 19.7, submittted by this same team quespa. 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 (hrls) is spanish. it is a highly inflective language based on its suffixes which agglutinate and found to be similar to other languages like finnish. it is worthwhile to note that previous work @xcite has been somewhat successful in identifying the inflectional properties of quechua such as agglutination where another hrls, namely finnish, can aid for translation purposes achieving nearly 20 bleu on religiousbased (text-only) tasks. 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 a "southern" languages. we label the data set with que -the iso norm for quechua ii mixtures. the quespa team this year consists of four organizers from four different institutions: northeastern university, pompeu fabra university, carnegie melon university and university of pretoria. a new organizer has been introduced this year who has expertise in machine translation (mt) of african languages. all of the iwslt 2024 organizers have continued to work on the project with exception of one. three of the four organizers have had experience with the que-spa language pair in the past and submitted have already submitted three times to iwslt, making this article the fourth submission with an increase of bleu score for each year's submission. we report the quespa consortium submission for the iwslt 2025 and once again focus on the low-resource task at hand by combining all the two dialects quechua i and ii into one. however, we specifically make use of the quechua ii variant in collao (iso:quz), given the discovery of a new corpus. the rest of this article is organized as follows. section 2 presents the related work. since we would like to highlight the addition of our mt comparisons and systems by a new author, we present a section dedicated to the mt delivery in section 3.1. afterwards, we present experiments for the for que-spa low-resource track are presented in section 3 and present their results in section 4 provides. ## related work in this section, we first cover the different approaches used in previous speech processing shared tasks for quechua (section 2.1). we then discuss prior work that used a similar strategy to our primary submission to the unconstrained track (section 2.2). ## quechua-spanish in this section we present our experiments for the que-spa dataset provided in the low-resource st track at iwslt 2025 foot_0 , identical to the dataset from iwslt 2024. as a reminder, the audio consists of contains 1 hour and 40 minutes of unconstrained speech along with its corresponding translations and nearly 48 hours of asr data (with transcriptions) from the siminichik @xcite corpus. additionally, an mt dataset is offered from previous neural mt work @xcite . the audio and corresponding transcriptions along with their translations are mostly made of radio broadcasting from the mountainous region in the andes, peru. this dataset has been used in other tasks but not in its entirety @xcite @xcite . this year there has been a new addition to the dataset provided by the task which is a machine-translated and post-edited text of the huqariq corpus @xcite that was used last year by this team @xcite for augmentation of the best performing t5 model @xcite . we present the three submissions for unconstrained task ony as this year the constrained task has been abandoned: we present the experimental settings and results for unconstrained systems starting off with the mt case studies in section 3.1. then, we describe the task further in section 3.2. primary, contrastive 1 and constrastive 2 descriptions are found in sections sections 3.3, 3.4 and 3.5, respectively. afterwards, we offer results and discussion in section 4. ## results and discussion results are presented in ## conclusion and future work our submission to the iwslt 2025 @xcite evaluation campaign for low-resource and dialect speech translation has included novelties based on the most state-of-the-art techniques for asr and st. the addition of three new characteristics: 1) a new quechua collao corpus (referred to as quz) and 2) the introduction of a stateless asr model (mamba) along with 3) a machine translation case study. these three new inclusions have brought to light what mt systems, corpus, and asr models work best with the language pair when compared to last year's work. next year, we plan to include more human annotation and experimentation with the model presented here since the bleu score achieved (26.7) warrant further investigation and annotation. we also believe that we have localized a speech translation recipe that we allow further iterations of data in the future to achieve even better performance.