  Extracting the speech of participants in a conversation amidst interfering
speakers and noise presents a challenging problem. In this paper, we introduce
the novel task of target conversation extraction, where the goal is to extract
the audio of a target conversation based on the speaker embedding of one of its
participants. To accomplish this, we propose leveraging temporal patterns
inherent in human conversations, particularly turn-taking dynamics, which
uniquely characterize speakers engaged in conversation and distinguish them
from interfering speakers and noise. Using neural networks, we show the
feasibility of our approach on English and Mandarin conversation datasets. In
the presence of interfering speakers, our results show an 8.19 dB improvement
in signal-to-noise ratio for 2-speaker conversations and a 7.92 dB improvement
for 2-4-speaker conversations. Code, dataset available at
https://github.com/chentuochao/Target-Conversation-Extraction.
