# Meeting Recognition with Continuous Speech Separation and Transcription-Supported Diarization

arXiv: https://arxiv.org/abs/2309.16482 · HTML: https://mlsanity.com/paper/2309.16482/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2406.08914/) (2024)
- [Error Analysis in a Modular Meeting Transcription System](https://mlsanity.com/paper/2509.10143/) (2025)
- [Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization](https://mlsanity.com/paper/2507.19356/) (2025)
- [CALM: Joint Contextual Acoustic-Linguistic Modeling for Personalization of Multi-Speaker ASR](https://mlsanity.com/paper/2601.22792/) (2026)
- [Diarization-Guided Qwen-ASR Adaptation for Multilingual Two-Speaker Conversational Speech](https://mlsanity.com/paper/2607.08208/) (2026)

## Abstract (arXiv)

We propose a modular pipeline for the single-channel separation, recognition, and diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset. Using a Continuous Speech Separation (CSS) system with a TF-GridNet separation architecture, followed by a speaker-agnostic speech recognizer, we achieve state-of-the-art recognition performance in terms of Optimal Reference Combination Word Error Rate (ORC WER). Then, a d-vector-based diarization module is employed to extract speaker embeddings from the enhanced signals and to assign the CSS outputs to the correct speaker. Here, we propose a syntactically informed diarization using sentence- and word-level boundaries of the ASR module to support speaker turn detection. This results in a state-of-the-art Concatenated minimum-Permutation Word Error Rate (cpWER) for the full meeting recognition pipeline.