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Nowadays, there is a strong need to deploy the target speaker separation(TSS) model on mobile devices with a limitation of the model size andcomputational 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 simulatingfrom the single-channel dataset, LibriPhone is made by simultaneously replayingpairs of utterances from LibriSpeech by two professional artificial heads andrecording by two built-in microphones of the mobile. Then, we propose alightweight time-frequency domain separation model, LSTM-Former, which is basedon the LSTM framework with source-to-noise ratio (SI-SNR) loss. For theexperiments on Libri-Phone, we explore the dual-channel LSTMFormer model and asingle-channel version by a random single channel of Libri-Phone. Experimentalresult shows that the dual-channel LSTM-Former outperforms the single-channelLSTMFormer with relative 25% improvement. This work provides a feasiblesolution for the TSS task on mobile devices, playing back and recordingmultiple data sources in real application scenarios for getting dual-channelreal data can assist the lightweight model to achieve higher performance.

Lightweight Dual-channel Target Speaker Separation for Mobile Voice Communication