Automatic Labelling of Speech Translation Errors

• URL: http://arxiv.org/abs/2606.06047v1
• Date: Thu, 04 Jun 2026 11:42:37 GMT
• Title: Automatic Labelling of Speech Translation Errors
• Abstract summary: Speech Translation Error Labelling (STEL) is a methodology for evaluating confidence and quality estimation of speech translations.<n>We create an annotation protocol, a small authentic end-to-end evaluation dataset, and we analyse how existing text-only and speech-processing systems perform the STEL task.
• Score: 11.234683445082188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Errors in speech translations reduce trustworthiness of Speech Translation (ST) systems and can have serious consequences. Yet currently there is no established methodology for evaluating confidence and quality estimation of speech translations. To initiate progress in this direction, we propose Speech Translation Error Labelling (STEL). We create an annotation protocol, a small authentic end-to-end evaluation dataset, and we analyse how existing text-only and speech-processing systems perform the STEL task. Our results show that text-only XCOMET and multimodal LLM Qwen2.5-Omni are able to perform the STEL task in roughly half the precision of humans. We also find that direct speech processing is necessary for the STEL task, and that the current text-only and speech-processing systems are complementary in labelling translation-only vs. speech-processing errors in ST.

Related papers

• Streaming Speech-to-Text Translation with a SpeechLLM [21.176799517305096]
  SpeechLLM promises to exploit paralinguistic information in the speech and to reduce cascaded errors.<n>Existing SpeechLLM systems are slow since they wait for a complete utterance of audio before outputting a translation.<n>This work proposes an LLM-based architecture for real streaming speech-to-text translation.<n>In experiments on different language pairs, the system achieves a translation quality close to the non-streaming baseline, but with a latency of only 1-2 seconds.
  arXiv  Detail & Related papers  (2026-05-14T12:32:57Z)
• MOSS-Speech: Towards True Speech-to-Speech Models Without Text Guidance [66.74042564585942]
  MOSS-Speech is a true speech-to-speech large language model that directly understands and generates speech without relying on text guidance.<n>Our work establishes a new paradigm for expressive and efficient end-to-end speech interaction.
  arXiv  Detail & Related papers  (2025-10-01T04:32:37Z)
• Scheduled Interleaved Speech-Text Training for Speech-to-Speech Translation with LLMs [41.088390995105826]
  Speech-to-speech translation (S2ST) has been advanced with large language models (LLMs)<n>LLMs are trained on text-only data, which presents challenges to adapt them to speech modality with limited speech-to-speech data.<n>We propose scheduled interleaved speech--text training in this study.
  arXiv  Detail & Related papers  (2025-06-12T02:24:44Z)
• Towards Unsupervised Speech Recognition Without Pronunciation Models [57.222729245842054]
  In this article, we tackle the challenge of developing ASR systems without paired speech and text corpora.<n>We experimentally demonstrate that an unsupervised speech recognizer can emerge from joint speech-to-speech and text-to-text masked token-infilling.<n>This innovative model surpasses the performance of previous unsupervised ASR models under the lexicon-free setting.
  arXiv  Detail & Related papers  (2024-06-12T16:30:58Z)
• DiariST: Streaming Speech Translation with Speaker Diarization [53.595990270899414]
  We propose DiariST, the first streaming ST and SD solution. It is built upon a neural transducer-based streaming ST system and integrates token-level serialized output training and t-vector. Our system achieves a strong ST and SD capability compared to offline systems based on Whisper, while performing streaming inference for overlapping speech.
  arXiv  Detail & Related papers  (2023-09-14T19:33:27Z)
• Simple and Effective Unsupervised Speech Translation [68.25022245914363]
  We study a simple and effective approach to build speech translation systems without labeled data. We present an unsupervised domain adaptation technique for pre-trained speech models. Experiments show that unsupervised speech-to-text translation outperforms the previous unsupervised state of the art.
  arXiv  Detail & Related papers  (2022-10-18T22:26:13Z)
• SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data [100.46303484627045]
  We propose a cross-modal Speech and Language Model (SpeechLM) to align speech and text pre-training with a pre-defined unified representation. Specifically, we introduce two alternative discrete tokenizers to bridge the speech and text modalities. We evaluate SpeechLM on various spoken language processing tasks including speech recognition, speech translation, and universal representation evaluation framework SUPERB.
  arXiv  Detail & Related papers  (2022-09-30T09:12:10Z)
• Unified Speech-Text Pre-training for Speech Translation and Recognition [113.31415771943162]
  We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four self-supervised and supervised subtasks for cross modality learning. It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset.
  arXiv  Detail & Related papers  (2022-04-11T20:59:51Z)
• SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training [33.02912456062474]
  We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech. We demonstrate that incorporating both speech and text data during pre-training can significantly improve downstream quality on CoVoST2 speech translation.
  arXiv  Detail & Related papers  (2021-10-20T00:59:36Z)
• Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration [62.75234183218897]
  We propose a one-stage context-aware framework to generate natural and coherent target speech without any training data of the speaker. We generate the mel-spectrogram of the edited speech with a transformer-based decoder. It outperforms a recent zero-shot TTS engine by a large margin.
  arXiv  Detail & Related papers  (2021-09-12T04:17:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.