Automatic speaker recognition from speech signal using bidirectional long‐short‐term memory recurrent neural network. (23rd January 2020)

• Record Type: Journal Article
• Authors: Devi, Kharibam Jilenkumari; Thongam, Khelchandra
• Is Part Of: Computational intelligence. Volume 39:Number 2(2023)
• Page Start: 170
• Page End: 193
• Publication Date: 2020-01-23
• Subjects: Mel‐frequency cepstral coefficient -- probabilistic principal component analysis -- recurrent neural network‐bidirectional long short term memory -- Wiener filter algorithm; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12278
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment and test, henceforth minimizing the identification execution as extreme vocal exertion is required during shouting. Speaker recognition requires more time for classification of data, accuracy is optimized, and the low root‐mean‐square error rate is the major problem. The objective of this work is to develop an efficient system of speaker recognition. In this work, an improved method of Wiener filter algorithm is applied for better noise reduction. To obtain the essential feature vector values, Mel‐frequency cepstral coefficient feature extraction method is used on the noise‐removed signals. Furthermore, input samples are created by using these extracted features after the dimensions have been reduced using probabilistic principal component analysis. Finally, recurrent neural network‐bidirectional long‐short‐term memory is used for the classification to improve the prediction accuracy. For checking the effectiveness, the proposed work is compared with the existing methods based on accuracy, sensitivity, and error rate. The results obtained with the proposed method demonstrate an accuracy of 95.77%.