Automatic Speech Recognition (ASR) can be a valuable tool to document endangered languages. However, building ASR tools for these languages poses several difficult research challenges, notably data scarcity. In this paper, we show the whole process of creating a useful ASR tool for language documentation scenarios. We publish the first speech corpus for Khinalug, an endangered language spoken in Northern Azerbaijan. The corpus consists of 2.67 hours of labeled data from recordings of spontaneous speech about various topics. As Khinalug is an extremely low-resource language, we investigate the benefits of multilingual models for self-supervised learning and supervised learning and achieve the performance of 6.65 Character Error Rate (CER) points and 25.53 Word Error Rate (WER) points. The benefits of multilingual models are further validated through experimentation with three additional under-resourced languages. Lastly, this work conducts quality assessments with linguists on new recordings to investigate the model’s usefulness in language documentation. We observe an evident degradation for new recordings, indicating the importance of enhancing model robustness. In addition, we find the inaudible content is the main cause of wrong ASR predictions, suggesting relating work on incorporating contextual information.

Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST). In this paper, we present KIT’s offline submission in the constrained + LLM track by incorporating recently proposed techniques that can be added to any cascaded speech translation. Specifically, we integrate Mistral-7B into our system to enhance it in two ways. Firstly, we refine the ASR outputs by utilizing the N-best lists generated by our system and fine-tuning the LLM to predict the transcript accurately. Secondly, we refine the MT outputs at the document level by fine-tuning the LLM, leveraging both ASR and MT predictions to improve translation quality. We find that integrating the LLM into the ASR and MT systems results in an absolute improvement of 0.3% in Word Error Rate and 0.65% in COMET for tst2019 test set. In challenging test sets with overlapping speakers and background noise, we find that integrating LLM is not beneficial due to poor ASR performance. Here, we use ASR with chunked long-form decoding to improve context usage that may be unavailable when transcribing with Voice Activity Detection segmentation alone.

This paper presents KIT’s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.

The challenge of low-latency speech translation has recently draw significant interest in the research community as shown by several publications and shared tasks. Therefore, it is essential to evaluate these different approaches in realistic scenarios. However, currently only specific aspects of the systems are evaluated and often it is not possible to compare different approaches. In this work, we propose the first framework to perform and evaluate the various aspects of low-latency speech translation under realistic conditions. The evaluation is carried out in an end-to-end fashion. This includes the segmentation of the audio as well as the run-time of the different components. Secondly, we compare different approaches to low-latency speech translation using this framework. We evaluate models with the option to revise the output as well as methods with fixed output. Furthermore, we directly compare state-of-the-art cascaded as well as end-to-end systems. Finally, the framework allows to automatically evaluate the translation quality as well as latency and also provides a web interface to show the low-latency model outputs to the user.