Performance Evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0201439" target="_blank" >RIV/00216305:26230/26:0201439 - isvavai.cz</a>
Result on the web
<a href="https://www.fit.vut.cz/research/group/speech/public/publi/2025/kumar_interspeech2025_co-author_Motlicek.pdf" target="_blank" >https://www.fit.vut.cz/research/group/speech/public/publi/2025/kumar_interspeech2025_co-author_Motlicek.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSPW65056.2025.11010998" target="_blank" >10.1109/ICASSPW65056.2025.11010998</a>
Alternative languages
Result language
angličtina
Original language name
Performance Evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
Original language description
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations on in-domain data, such as changes in speech rate or additive noise, can significantly degrade performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING WORKSHOPS, ICASSPW
ISBN
979-8-3315-1932-2
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
1-5
Publisher name
IEEE
Place of publication
Hyderabad, Indická republika
Event location
Hyderabad, Indická republika
Event date
Apr 6, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001547041200001