Speaker activity driven neural speech extraction

článek ve sborníku ve WoS nebo Scopus

angličtina

Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest. Various clues have been investigated such as pre-recorded enrollment utterances, direction information, or video of the target speaker. In this paper, we explore the use of speaker activity information as an auxiliary clue for single-channel neural network-based speech extraction. We propose a speaker activity driven speech extraction neural network (ADEnet) and show that it can achieve performance levels competitive with enrollmentbased approaches, without the need for pre-recordings. We further demonstrate the potential of the proposed approach for processing meeting-like recordings, where speaker activity obtained from a diarization system is used as a speaker clue for ADEnet. We show that this simple yet practical approach can successfully extract speakers after diarization, which leads to improved ASR performance when using a single microphone, especially in high overlapping conditions, with relative word error rate reduction of up to 25 %.

Speech extraction, Speaker activity, Speech enhancement, Meeting recognition, Neural network

DELCROIX, M.; ŽMOLÍKOVÁ, K.; OCHIAI, T.; KINOSHITA, K.; NAKATANI, T.

6. 6. 2021

IEEE Signal Processing Society

Toronto

978-1-7281-7605-5

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

6099

6103

5

https://www.fit.vut.cz/research/publication/12479/

delcroix_icassp2021_09414998.pdf