How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not

The remarkable performance achieved by Large Language Models (LLM) has driven research efforts to leverage them for a wide range of tasks and input modalities. In speech-to-text (S2T) tasks, the emerging solution consists of projecting the output of the encoder of a Speech Foundational Model (SFM) i...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Verdini, Francesco, Melucci, Pierfrancesco, Perna, Stefano, Cariaggi, Francesco, Gaido, Marco, Papi, Sara, Mazurek, Szymon, Kasztelnik, Marek, Bentivogli, Luisa, Bratières, Sébastien, Merialdo, Paolo, Scardapane, Simone
Format: Article
Language:English
Subjects:
Adapters
Automatic speech recognition
Large language models
Modules
Online Access:Get full text
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Summary:The remarkable performance achieved by Large Language Models (LLM) has driven research efforts to leverage them for a wide range of tasks and input modalities. In speech-to-text (S2T) tasks, the emerging solution consists of projecting the output of the encoder of a Speech Foundational Model (SFM) into the LLM embedding space through an adapter module. However, no work has yet investigated how much the downstream-task performance depends on each component (SFM, adapter, LLM) nor whether the best design of the adapter depends on the chosen SFM and LLM. To fill this gap, we evaluate the combination of 5 adapter modules, 2 LLMs (Mistral and Llama), and 2 SFMs (Whisper and SeamlessM4T) on two widespread S2T tasks, namely Automatic Speech Recognition and Speech Translation. Our results demonstrate that the SFM plays a pivotal role in downstream performance, while the adapter choice has moderate impact and depends on the SFM and LLM.
ISSN:2331-8422