Journal article
Correlation distance skip connection denoising autoencoder (CDSK-DAE) for speech feature enhancement
Applied acoustics, v 163, 107213
Jun 2020
Abstract
Performance of learning based Automatic Speech Recognition (ASR) is susceptible to noise, especially when it is introduced in the testing data while not presented in the training data. This work focuses on a feature enhancement for noise robust end-to-end ASR system by introducing a novel variant of denoising autoencoder (DAE). The proposed method uses skip connections in both encoder and decoder sides by passing speech information of the target frame from input to the model. It also uses a new objective function in training model that uses a correlation distance measure in penalty terms by measuring dependency of the latent target features and the model (latent features and enhanced features obtained from the DAE). Performance of the proposed method was compared against a conventional model and a state of the art model under both seen and unseen noisy environments of 7 different types of background noise with different SNR levels (0, 5, 10 and 20 dB). The proposed method also is tested using linear and non-linear penalty terms as well, where, they both show an improvement on the overall average WER under noisy conditions both seen and unseen in comparison to the state-of-the-art model.
Metrics
Details
- Title
- Correlation distance skip connection denoising autoencoder (CDSK-DAE) for speech feature enhancement
- Creators
- Alzahra Badi - Korea UniversitySangwook Park - Johns Hopkins UniversityDavid K. Han - DEVCOM Army Research LaboratoryHanseok Ko - Korea University
- Publication Details
- Applied acoustics, v 163, 107213
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000521507200002
- Scopus ID
- 2-s2.0-85078119404
- Other Identifier
- 991021931085004721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Acoustics