# NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription

arXiv: https://arxiv.org/abs/2401.08887 · HTML: https://mlsanity.com/paper/2401.08887/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [Continuous Speech Separation with Conformer](https://mlsanity.com/paper/2008.05773/) (2020)
- [Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis](https://mlsanity.com/paper/2011.02014/) (2020)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [Advances in Online Audio-Visual Meeting Transcription](https://mlsanity.com/paper/1912.04979/) (2019)
- [The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios](https://mlsanity.com/paper/2306.13734/) (2023)
- [DiPCo -- Dinner Party Corpus](https://mlsanity.com/paper/1909.13447/) (2019)
- [The Rise of the AI Co-Pilot: Lessons for Design from Aviation and Beyond](https://mlsanity.com/paper/2311.14713/) (2023)
- [Multi-scale Speaker Diarization with Dynamic Scale Weighting](https://mlsanity.com/paper/2203.15974/) (2022)
- [Estimating & Mitigating the Impact of Acoustic Environments on Machine-to-Machine Signalling](https://mlsanity.com/paper/1908.04672/) (2019)

## Built on by (most cited first, 21 of 21)

- [Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models](https://mlsanity.com/paper/2506.18623/) (2025)
- [TurnBench: A Multi-Domain Benchmark for Turn-Taking Dynamics in Spoken Dialogue](https://mlsanity.com/paper/2608.25218/) (2026)
- [Mamba-based Segmentation Model for Speaker Diarization](https://mlsanity.com/paper/2410.06459/) (2024)
- [SDBench: A Comprehensive Benchmark Suite for Speaker Diarization](https://mlsanity.com/paper/2507.16136/) (2025)
- [Summary of the NOTSOFAR-1 Challenge: Highlights and Learnings](https://mlsanity.com/paper/2501.17304/) (2025)
- [Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges](https://mlsanity.com/paper/2507.18161/) (2025)
- [Adapting Diarization-Conditioned Whisper for End-to-End Multi-Talker Speech Recognition](https://mlsanity.com/paper/2510.03723/) (2025)
- [SE-DiCoW: Self-Enrolled Diarization-Conditioned Whisper](https://mlsanity.com/paper/2601.19194/) (2026)
- [Adaptive Turn-Taking for Real-time Multi-Party Voice Agents](https://mlsanity.com/paper/2606.13544/) (2026)
- [ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings](https://mlsanity.com/paper/2406.03251/) (2024)
- [Talking to Robots: A Practical Examination of Speech Foundation Models for HRI Applications](https://mlsanity.com/paper/2508.17753/) (2025)
- [LOTUSDIS: A Thai far-field meeting corpus for robust conversational ASR](https://mlsanity.com/paper/2509.18722/) (2025)
- [Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages](https://mlsanity.com/paper/2607.23808/) (2026)
- [CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR](https://mlsanity.com/paper/2601.22792/) (2026)
- [Who Spoke What When? Evaluating Spoken Language Models for Conversational ASR with Semantic and Overlap-Aware Metrics](https://mlsanity.com/paper/2603.22709/) (2026)
- [MoDiCoL: A Modular Diagnostic Continual Learning Dataset for Robust Speech Recognition](https://mlsanity.com/paper/2606.14459/) (2026)
- [Evaluating Large Language Models Abilities for Addressee, Turn-change, and Next Speaker Prediction in Meetings](https://mlsanity.com/paper/2606.17542/) (2026)
- [Transsion's Speaker-Attributed Multilingual ASR System for the MLC-SLM 2026 Challenge](https://mlsanity.com/paper/2609.20833/) (2026)
- [Who Said What, and Will It Be Remembered? Evaluating Persistent Speaker Attribution Across Meetings](https://mlsanity.com/paper/2609.39344/) (2026)
- [FASTDIAR: Frame-level speaker encoder for Streaming Diarization](https://mlsanity.com/paper/2610.02941/) (2026)
- [Anuvani-Diarization: Zero Shot Speaker Diarization and Annotation Conventions Across 22 Languages](https://mlsanity.com/paper/2610.20327/) (2026)

## Abstract (arXiv)

We introduce the first Natural Office Talkers in Settings of Far-field Audio Recordings (``NOTSOFAR-1'') Challenge alongside datasets and baseline system. The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics. It is recorded across 30 conference rooms, featuring 4-8 attendees and a total of 35 unique speakers. Second, a 1000-hour simulated training dataset, synthesized with enhanced authenticity for real-world generalization, incorporating 15,000 real acoustic transfer functions. The tasks focus on single-device DASR, where multi-channel devices always share the same known geometry. This is aligned with common setups in actual conference rooms, and avoids technical complexities associated with multi-device tasks. It also allows for the development of geometry-specific solutions. The NOTSOFAR-1 Challenge aims to advance research in the field of distant conversational speech recognition, providing key resources to unlock the potential of data-driven methods, which we believe are currently constrained by the absence of comprehensive high-quality training and benchmarking datasets.