  Target-speaker automatic speech recognition (ASR) aims to transcribe the
desired speech of a target speaker from multi-talker overlapped utterances.
Most of the existing target-speaker ASR (TS-ASR) methods involve either
training from scratch or fully fine-tuning a pre-trained model, leading to
significant training costs and becoming inapplicable to large foundation
models. This work leverages prompt tuning, a parameter-efficient fine-tuning
approach, to extend Whisper, a large-scale single-talker ASR model, to TS-ASR.
Variants of prompt tuning approaches along with their configurations are
explored and optimized for TS-ASR.Experimental results show that prompt tuning
can achieve performance comparable to state-of-the-art full training approaches
while only requiring about 1\% of task-specific model parameters. Notably, the
original Whisper's features, such as inverse text normalization and timestamp
tagging, are retained in target-speaker ASR, keeping the generated
transcriptions natural and informative.
