From Speech to Summary in Turkmen: A Parameter-Efficient Neural Pipeline

This paper presents the development of a neural model pipeline for automatic speech recognition (ASR) and text summarization in Turkmen, a low-resource language with agglutinative morphology. For the ASR task, the MMS-1b-all model (Meta) was employed with LoRA adaptation and CTC decoding, fine-tuned on the Common Voice corpus (2733 samples). For summarization, the mBART-50-large model was used with Turkmen-specific tokenization and was trained on a news text corpus (10,248 samples). The following results were achieved: WER = 17.59% for ASR (baseline model: 107.33%) and ROUGE-L = 0.4255 for summarization (zero-shot baseline: 0.2294). The scientific contribution is the creation of a parameter-efficient neural pipeline for speech-to-summary for Turkmen. The developed system can be applied to automated meeting transcription and text data processing in the Turkmen language.