Automatic Speech Recognition: Factors Reducing Accuracy When Working with a Regional Variant of the Russian Language (using Material from Dagestan)
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Abstract
We evaluate the quality of automatic speech recognition (ASR) for Standard Russian and its Dagestani regional variety – a contact idiom formed in the multilingual environment of Dagestan. We tested ten off-the-shelf ASR configurations in a zero-shot setting (without fine-tuning) representing three architectural approaches (Conformer+RNN-T, Conformer+CTC, and Encoder-Decoder Transformer) on datasets of Standard Russian (GOLOS, RuDevices; approx. 20K and 90K utterances respectively) and Dagestani Russian (DaGRuS; approx. 40K utterances). All models demonstrate a word error rate (WER) degradation of 33%–50% when evaluated on Dagestani Russian, highlighting a severe domain shift problem. Error structure analysis reveals three primary degradation mechanisms: “Russification” (systematic replacement of Dagestani forms with phonetically similar Standard Russian words); deletion of discourse markers and out-of-vocabulary (OOV) terms such as ethnonyms and toponyms; and generative model hallucinations on incomprehensible audio segments. We found that end-to-end architectures suffer less degradation than cascaded ones (33%–35% vs. 40%–42%), yet no model achieves acceptable performance (minimum WER ≈ 43%). Finally, we formulate recommendations for designing ASR systems robust to the regional variability of the Russian language.
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