Paper Info
Title
Front-End Feature Compensation For Noise Robust Speech Emotion Recognition
Abstract
Robust feature compensation and selection are important aspects of noisy speech emotion recognition (SER) task, especially in mismatched condition, when the models are trained on clean speech and tested in the noisy scenarios. Here we propose the use of front-end feature compensation techniques based on Vector Taylor Series (VTS) expansion and VTS with auditory masking (VTS-AM) to improve the performance of SER systems. On top of VTS and VTS-AM, we compare the performances of log-compression and root-compression to the mel-filter-bank energies. Further, we demonstrate the benefit of feature selection applied to the non-MFCC high-level descriptors in conjunction with VTS, VTS-AM and root compression. The system performance is compared with popular Non-negative Matrix Factorization (NMF) based enhancement and energy based voice activity detector (VAD) technique, which discards silence or noisy frames in the spoken utterances. To demonstrate the efficacy of our proposed techniques, extensive experiments are conducted on 2 standard datasets (EmoDB and IEMOCAP), contaminated with 5 types of noise (Babble, F-16, Factory, Volvo, and HF-channel) from the Noisex-92 noise database at 5 SNR levels (0dB, 5dB, 10dB, 15dB and 20dB).
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902981
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Keywords
Field
DocType
Emotion recognition, Noisy speech, Feature compensation, Auditory masking, Vector Taylor Series
Front and back ends,Auditory masking,Feature selection,Computer science,Matrix decomposition,Speech recognition,Non-negative matrix factorization,Feature compensation,Detector,Taylor series
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Meghna Pandharipande100.34
Rupayan Chakraborty200.68
Ashish Panda320.75
Biswajit Das400.34
Sunil Kumar Kopparapu54225.18