# Sequence-level Speaker Change Detection with Difference-based Continuous Integrate-and-fire

arXiv: https://arxiv.org/abs/2206.13110 · HTML: https://mlsanity.com/paper/2206.13110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [SpeechCLIP+: Self-supervised multi-task representation learning for speech via CLIP and speech-image data](https://mlsanity.com/paper/2402.06959/) (2024)

## Abstract (arXiv)

Speaker change detection is an important task in multi-party interactions such as meetings and conversations. In this paper, we address the speaker change detection task from the perspective of sequence transduction. Specifically, we propose a novel encoder-decoder framework that directly converts the input feature sequence to the speaker identity sequence. The difference-based continuous integrate-and-fire mechanism is designed to support this framework. It detects speaker changes by integrating the speaker difference between the encoder outputs frame-by-frame and transfers encoder outputs to segment-level speaker embeddings according to the detected speaker changes. The whole framework is supervised by the speaker identity sequence, a weaker label than the precise speaker change points. The experiments on the AMI and DIHARD-I corpora show that our sequence-level method consistently outperforms a strong frame-level baseline that uses the precise speaker change labels.