@inproceedings{yoshioka2019low-latency,
author = {Yoshioka, Takuya and Chen, Zhuo and Liu, Changliang and Xiao, Xiong and Erdogan, Hakan and Dimitriadis, Dimitrios},
title = {Low-Latency Speaker-Independent Continuous Speech Separation},
organization = {IEEE},
booktitle = {ICASSP 2019},
year = {2019},
month = {April},
abstract = {Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech segment. A separated, or cleaned, version of each utterance is
generated from one of SI-CSS's output channels nondeterministically without being split up and distributed to multiple channels. A typical application scenario is transcribing multi-party conversations, such as meetings, recorded with microphone arrays. The output signals can be simply sent to a speech recognition engine because they do not include speech overlaps. The previous SI-CSS method uses a neural network trained with permutation invariant training and a data-driven beamformer and thus requires much processing latency. This paper proposes a low-latency SI-CSS method whose performance is comparable to that of the previous method in a microphone array-based meeting transcription task. This is achieved (1) by using a new speech separation network architecture combined with a double buffering scheme and (2) by performing enhancement with a set of fixed beamformers followed by a neural post-filter.},
publisher = {IEEE},
url = {http://approjects.co.za/?big=en-us/research/publication/low-latency-speaker-independent-continuous-speech-separation/},
}