Speech-aware LLMs show weak speaker verification, new method improves performance

Researchers have developed a new method to evaluate and enhance the speaker verification capabilities of speech-aware Large Language Models (LLMs). Initial benchmarks revealed that current speech-aware LLMs exhibit weak speaker discrimination, with error rates exceeding 20% on the VoxCeleb1 dataset. To address this, a lightweight augmentation technique was introduced, which injects speaker embeddings into an LLM and trains only LoRA adapters. This approach, demonstrated on TinyLLaMA-1.1B, resulted in an ECAPA-LLM that achieved a 1.03% error rate on VoxCeleb1-E, nearing the performance of dedicated speaker verification systems while retaining a natural language interface. AI

IMPACT This research could lead to LLMs with enhanced capabilities for understanding and verifying speaker identity, potentially impacting voice assistants and security applications.

RANK_REASON The cluster contains an academic paper detailing a new method for evaluating and augmenting LLMs for speaker verification. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• ECAPA-LLM
• ECAPA-TDNN
• Lora
• Speech-Aware LLMs
• Thomas Thebaud
• TinyLLaMA-1.1B
• VoxCeleb1
• VoxCeleb1-E

• paper
• model release

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