3 papers
cs.CL2026
Fairness Beyond a Single Run: Training-Seed Variability in Speech LLM Adaptation
Srishti Ginjala, Eric Fosler-Lussier, Srinivasan Parthasarathy
Demographic fairness gaps in automatic speech recognition are almost always reported from a single training run. We fine-tune the Q-former projector and LoRA adapters of a speech L…
cs.CL2026
Temporal Taxation Compounds Under Post-Training Compression of Whisper Models
Srishti Ginjala, Eric Fosler-Lussier, Christopher W. Myers +1
Automatic speech recognition models are audited for demographic fairness at full precision, yet the models that ship to production have been quantized, pruned, and distilled. We as…
cs.CL2026
Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition
Srishti Ginjala, Eric Fosler-Lussier, Christopher W. Myers +1
As pretrained large language models replace task-specific decoders in speech recognition, a critical question arises: do their text-derived priors make recognition fairer or more b…