# Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation

arXiv: https://arxiv.org/abs/2411.17846 · HTML: https://mlsanity.com/paper/2411.17846/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2024.

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

- [Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models](https://mlsanity.com/paper/2606.10046/) (2026)

## Abstract (arXiv)

End-to-end transformer-based automatic speech recognition (ASR) systems often capture multiple speech traits in their learned representations that are highly entangled, leading to a lack of interpretability. In this study, we propose the explainable Disentangled-Transformer, which disentangles the internal representations into sub-embeddings with explicit content and speaker traits based on varying temporal resolutions. Experimental results show that the proposed Disentangled-Transformer produces a clear speaker identity, separated from the speech content, for speaker diarization while improving ASR performance.