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eess.AS2026

AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models

Natarajan Balaji Shankar, Zilai Wang, Zihan Wang +3

Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs contain no speech, weak acoustic…

eess.AS2026

Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs

Mohan Shi, Zilai Wang, Natarajan Balaji Shankar +3

Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown…

eess.AS2026

Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains

Zilai Wang, Natarajan Balaji Shankar, Mohan Shi +2

Speech foundation models often struggle in low-resource domains due to domain mismatch and data scarcity. We propose Gumbel-BEARD, a domain adaptation framework that automates Whis…

eess.AS2026

GC-LoRA: Gated Convolutional LoRA for Parameter-Efficient Acoustic Adaptation

Natarajan Balaji Shankar, Zilai Wang, Kaiyuan Zhang +2

Transformer-based Speech Foundation Models excel in most Automatic Speech Recognition tasks but often suffer performance degradation when applied to domains with mismatched acousti…

eess.AS2026

Entropy-Aware Domain-Routed Mixture-of-Experts Speech-LLM Framework: A Case Study of Multi-Domain Child-Adult ASR

Mohan Shi, Kaiyuan Zhang, Zilai Wang +3

While Speech Large Language Models (Speech-LLMs) have achieved strong performance on adult Automatic Speech Recognition (ASR), their effectiveness on child speech remains under-exp…

eess.AS2024

Advancing Multi-talker ASR Performance with Large Language Models

Mohan Shi, Zengrui Jin, Yaoxun Xu +6

Recognizing overlapping speech from multiple speakers in conversational scenarios is one of the most challenging problem for automatic speech recognition (ASR). Serialized output t…