Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Hung, Jeih-weih; Lin, Jung-Shan; Wu, Po-Jen

doi:10.3390/asi1030028

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Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

Jeih-weih Hung

^*,

Jung-Shan Lin

and

Po-Jen Wu

Department of Electrical Engineering, National Chi Nan University, 545, Nantou County, Taiwan

Author to whom correspondence should be addressed.

Appl. Syst. Innov. 2018, 1(3), 28; https://doi.org/10.3390/asi1030028

Received: 6 April 2018 / Revised: 11 August 2018 / Accepted: 12 August 2018 / Published: 15 August 2018

(This article belongs to the Special Issue Selected papers from IEEE ICASI 2018)

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Abstract

In recent decades, researchers have been focused on developing noise-robust methods in order to compensate for noise effects in automatic speech recognition (ASR) systems and enhance their performance. In this paper, we propose a feature-based noise-robust method that employs a novel data analysis technique—robust principal component analysis (RPCA). In the proposed scenario, RPCA is employed to process a noise-corrupted speech feature matrix, and the obtained sparse partition is shown to reveal speech-dominant characteristics. One apparent advantage of using RPCA for enhancing noise robustness is that no prior knowledge about the noise is required. The proposed RPCA-based method is evaluated with the Aurora-4 database and a task using a state-of-the-art deep neural network (DNN) architecture as the acoustic models. The evaluation results indicate that the newly proposed method can provide the original speech feature with significant recognition accuracy improvement, and can be cascaded with mean normalization (MN), mean and variance normalization (MVN), and relative spectral (RASTA)—three well-known and widely used feature robustness algorithms—to achieve better performance compared with the individual component method. View Full-Text

Keywords: robust principal component analysis; noise robustness; filter-bank features; mel-frequency cepstral coefficients; deep neural network

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Hung, J.-w.; Lin, J.-S.; Wu, P.-J. Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network. Appl. Syst. Innov. 2018, 1, 28. https://doi.org/10.3390/asi1030028

AMA Style

Hung J-w, Lin J-S, Wu P-J. Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network. Applied System Innovation. 2018; 1(3):28. https://doi.org/10.3390/asi1030028

Chicago/Turabian Style

Hung, Jeih-weih, Jung-Shan Lin, and Po-Jen Wu. 2018. "Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network" Applied System Innovation 1, no. 3: 28. https://doi.org/10.3390/asi1030028

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Employing Robust Principal Component Analysis for Noise-Robust Speech Feature Extraction in Automatic Speech Recognition with the Structure of a Deep Neural Network

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