Paper Info
Title
Personalized speech enhancement: new models and Comprehensive evaluation
Abstract
Personalized speech enhancement (PSE) models utilize additional cues, such as speaker embeddings like d-vectors, to remove background noise and interfering speech in real-time and thus improve the speech quality of online video conferencing systems for various acoustic scenarios. In this work, we propose two neural networks for PSE that achieve superior performance to the previously proposed VoiceFilter. In addition, we create test sets that capture a variety of scenarios that users can encounter during video conferencing. Furthermore, we propose a new metric to measure the target speaker over-suppression (TSOS) problem, which was not sufficiently investigated before despite its critical importance in deployment. Besides, we propose multi-task training with a speech recognition back-end. Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.
Year
DOI
Venue
2022
10.1109/ICASSP43922.2022.9746962
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Keywords
DocType
ISSN
Speech enhancement,personalized speech enhancement,speaker embedding,automatic speech recognition,perceptual speech quality
Conference
1520-6149
ISBN
Citations 
PageRank 
978-1-6654-0541-6
2
0.37
References 
Authors
15
6
Name
Order
Citations
PageRank
Eskimez, S.E.1155.34
Takuya Yoshioka258549.20
Huaming Wang3132.35
Xiaofei Wang454.14
Zhuo Chen515324.33
Xuedong Huang61390283.19