FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition
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Computer Science > Computation and Language
Title:FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition
Abstract:Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We investigate speaker conditioning via Feature-wise Linear Modulation (FiLM), injecting x-vector-derived information into each transformer layer of a frozen ASR encoder to adapt internal representations to individual pathological speakers without modifying base model weights. We benchmark this for the ASR task against standard and parameter-efficient fine-tuning baselines, complemented by post-processing, on Spanish and English pathological speech. Additionally, we evaluate if the adapted model preserves the ability to answer speech-related questions. Results show that speaker-conditioned ASR is competitive with established adaptation strategies while retaining performance on non-conditioned speech.
| Comments: | Accepted in Odyssey 2026: The Speaker and Language Recognition Workshop |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.06211 [cs.CL] |
| (or arXiv:2606.06211v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06211
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Fernando López PhD(c) [view email][v1] Thu, 4 Jun 2026 14:20:11 UTC (207 KB)
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