Toward an Adaptive Speech-to-Summary Pipeline for Turkic Languages: Language-Specific ASR, Conditional Morphology-Aware Correction, Pivot Translation, and Summarization

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Speech-to-Summary cascade performance for low-resource, morphologically rich languages depends on interactions among language, recognition errors, and translation direction. This study develops an adaptive Speech-to-Summary pipeline for six Turkic languages—Azerbaijani, Kazakh, Kyrgyz, Turkish, Turkmen, and Uzbek—integrating ASR, conditional morphology-aware post-processing (MAPP), MT, pivot routing, and summarization. Whisper-family models lead external benchmarks for Turkish, Kazakh, and Azerbaijani; MMS for Kyrgyz, Uzbek, and Turkmen. The Uzbek benchmark-best model proves unreliable on real recordings, requiring a different deployed model. Pause-based segmentation reduces ASR errors for Kazakh, Azerbaijani, Uzbek, and Kyrgyz but is not universally beneficial. MAPP shows no consistent improvement on strong real-ASR outputs; only 3.6% of real Uzbek substitution errors are reachable by the correction rules, supporting conditional rather than mandatory correction. MT evaluation across 22 FLORES-200 directions reveals direction-dependent performance and sensitivity to script compatibility. Pivot selection is language-dependent: direct-Kazakh routing performs better for Kazakh, Turkmen, and Turkish, whereas Azerbaijani benefits from an English pivot. The results support replacing a fixed cascade with a configurable architecture selecting ASR, segmentation, correction, MT, and pivot routing by language, error patterns, and deployment constraints. Routing decisions are currently made offline based on empirical evidence; automatic routing is left for future work.