# Model-Agnostic and Language-Agnostic Voice Pipeline Improvement for the Agriculture Domain

arXiv: https://arxiv.org/abs/2609.20504 · HTML: https://mlsanity.com/paper/2609.20504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [Powerset multi-class cross entropy loss for neural speaker diarization](https://mlsanity.com/paper/2310.13025/) (2023)
- [Towards Building ASR Systems for the Next Billion Users](https://mlsanity.com/paper/2111.03945/) (2021)
- [DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement](https://mlsanity.com/paper/2305.08227/) (2023)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [Leveraging Self-Supervised Learning for Speaker Diarization](https://mlsanity.com/paper/2409.09408/) (2024)
- [Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems](https://mlsanity.com/paper/2409.06656/) (2024)
- [Multi-scale Speaker Diarization with Dynamic Scale Weighting](https://mlsanity.com/paper/2203.15974/) (2022)
- [SPRING-INX: A Multilingual Indian Language Speech Corpus by SPRING Lab, IIT Madras](https://mlsanity.com/paper/2310.14654/) (2023)
- [Vakyansh: ASR Toolkit for Low Resource Indic languages](https://mlsanity.com/paper/2203.16512/) (2022)
- [Benchmarking Automatic Speech Recognition for Indian Languages in Agricultural Contexts](https://mlsanity.com/paper/2602.03868/) (2026)
- [Fine-Tuning and Evaluating Conversational AI for Agricultural Advisory](https://mlsanity.com/paper/2603.03294/) (2026)

## Abstract (arXiv)

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.