Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2609.20504 (eess)
[Submitted on 17 Sep 2026]
Title:Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain
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Abstract:FarmerChat is Digital Green's AI-powered agricultural advisory assistant for smallholder farmers, who access it in their own language through text, voice, or photographs. Voice is a critical channel for this population, yet field-recorded speech is challenging for general-purpose automatic speech recognition (ASR) because recordings frequently contain machinery noise, background media, competing speakers, and domain-specific agricultural vocabulary. These conditions disproportionately affect crop, pest, chemical, and quantity terms that carry the meaning of a farmer's query.
We present a modular, model-agnostic pipeline for improving ASR quality in FarmerChat without fine-tuning or replacing the underlying ASR model. The pipeline combines gated audio enhancement, speaker diarization and target-speaker selection, ASR, domain-aware correction using a weighted agricultural lexicon, and a quality gate for detecting unreliable transcripts. Only the diarization stage is fine-tuned; all other stages use off-the-shelf models behind common interfaces.
We evaluate the pipeline on human-annotated FarmerChat recordings in Hindi, Telugu, and Odia using word error rate (WER) and a domain-weighted error rate that gives greater importance to agricultural terminology. The largest improvements occur on multi-speaker recordings, where target-speaker selection prevents competing speech from entering the transcript. Across the full corpus, the pipeline reduces WER by 16-23% relative on three cloud ASR models and by 5% on an on-device model. On multi-speaker recordings, the reductions are 32-42% for the cloud models and 16% for the on-device model. All reported reductions are statistically significant. These results show that targeted preprocessing, speaker selection, and domain-aware post-processing can substantially improve agricultural speech transcription while preserving the underlying ASR model.
| Comments: | 20 tables, 11 figures, 23 pages |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2609.20504 [eess.AS] |
| (or arXiv:2609.20504v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20504
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain, by Aakash Singh and 5 other authors
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