Noise robust speech encoding system in challenging acoustic conditions

• Published in: International journal of speech technology Vol. 27; no. 3; pp. 539 - 549
• Main Authors: Nagaraja, B. G., Yadava, G. Thimmaraja, Harshitha, K.
• Format: Journal Article
• Language: English
• Published: New York Springer US 2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Coding; Engineering; Kannada language; Neural networks; Noise levels; Noise prediction; Noise sensitivity; Signal quality; Signal to noise ratio; Signal,Image and Speech Processing; Social Sciences; Speech enhancement; Speech processing; Spoken language; Wireless communication systems; NOIZEUS; Kannada; DNN; Coding; PESQ; SNR; NCM; Enhancement; LPC
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-024-10119-3

Wireless communication systems are facing challenges in maintaining high-quality speech transmission due to the growing amount of data being transmitted. Linear predictive coding (LPC) is by far the most popular speech coding technique. A well-known shortcoming of LPC-based methods is their sensitivity to noise. In recent research, slight performance improvement is observed for LPC-based speech coding system under noisy environments. In this paper, we propose a pre-processing speech enhancement technique based on deep neural networks (DNN) to improve speech coding quality in negative signal-to-noise ratio (SNR) conditions. Furthermore, this study emphasizes the significance of phase information and underscores the potential benefits of employing DNN-based pre-processing modules to improve speech coding quality. We evaluate our proposed method using two databases, NOIZEUS and Kannada, which contain a wide range of noise levels. Experimental results show that our method outperforms traditional LPC, as demonstrated by the speech quality metrics.