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Speech separation remains an important topic for multi-speaker technologyresearchers. Convolution augmented transformers (conformers) have performedwell for many speech processing tasks but have been under-researched for speechseparation. Most recent state-of-the-art (SOTA) separation models have beentime-domain audio separation networks (TasNets). A number of successful modelshave made use of dual-path (DP) networks which sequentially process local andglobal information. Time domain conformers (TD-Conformers) are an analogue ofthe DP approach in that they also process local and global context sequentiallybut have a different time complexity function. It is shown that for realisticshorter signal lengths, conformers are more efficient when controlling forfeature dimension. Subsampling layers are proposed to further improvecomputational efficiency. The best TD-Conformer achieves 14.6 dB and 21.2 dBSISDR improvement on the WHAMR and WSJ0-2Mix benchmarks, respectively.

On Time Domain Conformer Models for Monaural Speech Separation in Noisy Reverberant Acoustic Environments