  Nowadays, there is a strong need to deploy the target speaker separation
(TSS) model on mobile devices with a limitation of the model size and
computational complexity. To better perform TSS for mobile voice communication,
we first make a dual-channel dataset based on a specific scenario, LibriPhone.
Specifically, to better mimic the real-case scenario, instead of simulating
from the single-channel dataset, LibriPhone is made by simultaneously replaying
pairs of utterances from LibriSpeech by two professional artificial heads and
recording by two built-in microphones of the mobile. Then, we propose a
lightweight time-frequency domain separation model, LSTM-Former, which is based
on the LSTM framework with source-to-noise ratio (SI-SNR) loss. For the
experiments on Libri-Phone, we explore the dual-channel LSTMFormer model and a
single-channel version by a random single channel of Libri-Phone. Experimental
result shows that the dual-channel LSTM-Former outperforms the single-channel
LSTMFormer with relative 25% improvement. This work provides a feasible
solution for the TSS task on mobile devices, playing back and recording
multiple data sources in real application scenarios for getting dual-channel
real data can assist the lightweight model to achieve higher performance.
