@inproceedings{dkhissi-etal-2026-sens,
title = "{SENS}-{ASR}: Semantic Embedding Injection in Neural-transducer for Streaming Automatic Speech Recognition",
author = "Dkhissi, Youness and
Vielzeuf, Valentin and
Allesiardo, Elys and
Larcher, Anthony",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.803/",
doi = "10.63317/2bpj98q88wzi",
pages = "10233--10241",
abstract = "Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to start transcribing the input stream before it is complete, i.e., the systems have to process a stream of inputs with a limited (or no) future context. Compared to offline mode, this reduction of the future context degrades the performance of Streaming-ASR systems, especially while working with low-latency constraint. In this work, we present SENS-ASR, an approach to enhance the transcription quality of Streaming-ASR by reinforcing the acoustic information with semantic information. This semantic information is extracted from the available past frame-embeddings by a context module. This module is trained using knowledge distillation from a sentence embedding Language Model fine-tuned on the training dataset transcriptions. Experiments on standard datasets show that SENS-ASR significantly improves the Word Error Rate on small-chunk streaming scenarios."
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%0 Conference Proceedings
%T SENS-ASR: Semantic Embedding Injection in Neural-transducer for Streaming Automatic Speech Recognition
%A Dkhissi, Youness
%A Vielzeuf, Valentin
%A Allesiardo, Elys
%A Larcher, Anthony
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F dkhissi-etal-2026-sens
%X Many Automatic Speech Recognition (ASR) applications require streaming processing of the audio data. In streaming mode, ASR systems need to start transcribing the input stream before it is complete, i.e., the systems have to process a stream of inputs with a limited (or no) future context. Compared to offline mode, this reduction of the future context degrades the performance of Streaming-ASR systems, especially while working with low-latency constraint. In this work, we present SENS-ASR, an approach to enhance the transcription quality of Streaming-ASR by reinforcing the acoustic information with semantic information. This semantic information is extracted from the available past frame-embeddings by a context module. This module is trained using knowledge distillation from a sentence embedding Language Model fine-tuned on the training dataset transcriptions. Experiments on standard datasets show that SENS-ASR significantly improves the Word Error Rate on small-chunk streaming scenarios.
%R 10.63317/2bpj98q88wzi
%U https://aclanthology.org/2026.lrec-1.803/
%U https://doi.org/10.63317/2bpj98q88wzi
%P 10233-10241
Markdown (Informal)
[SENS-ASR: Semantic Embedding Injection in Neural-transducer for Streaming Automatic Speech Recognition](https://aclanthology.org/2026.lrec-1.803/) (Dkhissi et al., LREC 2026)
ACL