Detail výsledku
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models
This paper explores speculative speech recognition (SSR),
where we empower conventional automatic speech recognition
(ASR) with speculation capabilities, allowing the recognizer to
run ahead of audio. We introduce a metric for measuring SSR
performance and we propose a model which does SSR by com
bining a RNN-Transducer-based ASR system with an audioprefixed language model (LM). The ASR system transcribes
ongoing audio and feeds the resulting transcripts, along with
an audiodependent prefix, to the LM, which speculates likely
completions for the transcriptions. We experiment with a variety of ASR datasets on which show the efficacy our method and
the feasibility of SSR as a method of reducing ASR latency.
low-latency speech recognition, speculative speech recognition, prefix language model, low-rank adaptation
@inproceedings{BUT193739,
author="YUSUF, B. and BASKAR, M. and ROSENBERG, A. and RAMABHADRAN, B.",
title="Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models",
booktitle="Proceedings of Interspeech 2024",
year="2024",
journal="Proceedings of Interspeech",
volume="2024",
number="9",
pages="792--796",
publisher="International Speech Communication Association",
address="Kos",
doi="10.21437/Interspeech.2024-298",
issn="1990-9772",
url="https://www.isca-archive.org/interspeech_2024/yusuf24_interspeech.pdf"
}