In-Context Learning Boosts Speech Recognition via Human-like Adaptation
  to Speakers and Language Varieties
Human listeners readily adjust to unfamiliar speakers and language varieties
through exposure, but do these adaptation benefits extend to state-of-the-art
spoken language models? We introduce a scalable framework that allows for
in-context learning (ICL) in Phi-4 Multimodal using interleaved task prompts
and audio-text pairs, and find that as few as 12 example utterances (~50
seconds) at inference time reduce word error rates by a relative 19.7% (1.2
pp.) on average across diverse English corpora. These improvements are most
pronounced in low-resource varieties, when the context and target speaker
match, and when more examples are provided--though scaling our procedure yields
diminishing marginal returns to context length. Overall, we find that our novel
ICL adaptation scheme (1) reveals a similar performance profile to human
listeners, and (2) demonstrates consistent improvements to automatic speech
recognition (ASR) robustness across diverse speakers and language backgrounds.
While adaptation succeeds broadly, significant gaps remain for certain
varieties, revealing where current models still fall short of human
flexibility. We release our prompts and code on GitHub.
682fcd1de495b6677027fe85
Nathan Roll
claimed_verified
2025-05-26T08:16:56.162Z
631395b2e90d5240127ca48f
https://cdn-avatars.huggingface.co/v1/production/uploads/1673106093213-631395b2e90d5240127ca48f.png
Nathan Roll
user
NathanRoll
682fcd1de495b6677027fe86
Calbert Graham
682fcd1de495b6677027fe87
Yuka Tatsumi
682fcd1de495b6677027fe88
Kim Tien Nguyen
682fcd1de495b6677027fe89
Meghan Sumner
682fcd1de495b6677027fe8a
Dan Jurafsky
In-Context Learning Boosts Speech Recognition via Human-like Adaptation
  to Speakers and Language Varieties
Human listeners readily adjust to unfamiliar speakers and language varieties
through exposure, but do these adaptation benefits extend to state-of-the-art
spoken language models? We introduce a scalable framework that allows for
in-context learning (ICL) in Phi-4 Multimodal using interleaved task prompts
and audio-text pairs, and find that as few as 12 example utterances (~50
seconds) at inference time reduce word error rates by a relative 19.7% (1.2
pp.) on average across diverse English corpora. These improvements are most
pronounced in low-resource varieties, when the context and target speaker
match, and when more examples are provided--though scaling our procedure yields
diminishing marginal returns to context length. Overall, we find that our novel
ICL adaptation scheme (1) reveals a similar performance profile to human
listeners, and (2) demonstrates consistent improvements to automatic speech
recognition (ASR) robustness across diverse speakers and language backgrounds.
While adaptation succeeds broadly, significant gaps remain for certain
varieties, revealing where current models still fall short of human
flexibility. We release our prompts and code on GitHub.
In-context learning in Phi-4 Multimodal demonstrates significant improvements in automatic speech recognition robustness with a small number of example utterances, showing a performance profile similar to human listeners.
in-context learning ICL Phi-4 Multimodal interleaved task prompts audio-text pairs word error rates automatic speech recognition ASR