Text-independent speaker recognition using LSTM-RNN and speech enhancement

• Published in: Multimedia tools and applications Vol. 79; no. 33-34; pp. 24013 - 24028
• Main Authors: El-Moneim, Samia Abd, Nassar, M. A., Dessouky, Moawad I., Ismail, Nabil A., El-Fishawy, Adel S., Abd El-Samie, Fathi E.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Biometrics; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature extraction; Multimedia; Multimedia Information Systems; Neural networks; Noise; Noise reduction; Recording; Recurrent neural networks; Short term; Signal processing; Special Purpose and Application-Based Systems; Speech; Speech processing; Speech recognition; Subtraction; Log-spectrum; MFCCs; Speech enhancement; Speaker recognition; Reverberation; LSTM-RNN; Spectrum
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08293-7

Speaker recognition revolution has lead to the inclusion of speaker recognition modules in several commercial products. Most published algorithms for speaker recognition focus on text-dependent speaker recognition. In contrast, text-independent speaker recognition is more advantageous as the client can talk freely to the system. In this paper, text-independent speaker recognition is considered in the presence of some degradation effects such as noise and reverberation. Mel-Frequency Cepstral Coefficients (MFCCs), spectrum and log-spectrum are used for feature extraction from the speech signals. These features are processed with the Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) as a classification tool to complete the speaker recognition task. The network learns to recognize the speakers efficiently in a text-independent manner, when the recording circumstances are the same. The recognition rate reaches 95.33% using MFCCs, while it is increased to 98.7% when using spectrum or log-spectrum. However, the system has some challenges to recognize speakers from different recording environments. Hence, different speech enhancement techniques, such as spectral subtraction and wavelet denoising, are used to improve the recognition performance to some extent. The proposed approach shows superiority, when compared to the algorithm of R. Togneri and D. Pullella (2011).