# Disentangling ASR and MT Errors in Speech Translation

arXiv: https://arxiv.org/abs/1709.00678 · HTML: https://mlsanity.com/paper/1709.00678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2017.

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

- [SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation](https://mlsanity.com/paper/2508.07781/) (2025)

## Abstract (arXiv)

The main aim of this paper is to investigate automatic quality assessment for spoken language translation (SLT). More precisely, we investigate SLT errors that can be due to transcription (ASR) or to translation (MT) modules. This paper investigates automatic detection of SLT errors using a single classifier based on joint ASR and MT features. We evaluate both 2-class (good/bad) and 3-class (good/badASR/badMT ) labeling tasks. The 3-class problem necessitates to disentangle ASR and MT errors in the speech translation output and we propose two label extraction methods for this non trivial step. This enables - as a by-product - qualitative analysis on the SLT errors and their origin (are they due to transcription or to translation step?) on our large in-house corpus for French-to-English speech translation.