  This paper investigates the possibility of extracting a target sentence from
multi-talker speech using only a keyword as input. For example, in social
security applications, the keyword might be "help", and the goal is to identify
what the person who called for help is articulating while ignoring other
speakers. To address this problem, we propose using the Transformer
architecture to embed both the keyword and the speech utterance and then rely
on the cross-attention mechanism to select the correct content from the
concatenated or overlapping speech. Experimental results on Librispeech
demonstrate that our proposed method can effectively extract target sentences
from very noisy and mixed speech (SNR=-3dB), achieving a phone error rate (PER)
of 26\%, compared to the baseline system's PER of 96%.
