End-to-end recurrent denoising autoencoder embeddings for speaker identification

• Published in: Neural computing & applications Vol. 33; no. 21; pp. 14429 - 14439
• Main Authors: Rituerto-González, Esther, Peláez-Moreno, Carmen
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Audio equipment; Background noise; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Feedback loops; Image Processing and Computer Vision; Noise; Noise reduction; Optimization; Original Article; Probability and Statistics in Computer Science; Representations; Spectrograms; Speech recognition; Noisy conditions; Denoising autoencoder; Speaker identification; Speaker embeddings; Stress; End-to-end model
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06083-7

Speech ‘in-the-wild’ is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as handcrafted features.