Publication: End-to-end recurrent denoising autoencoder embeddings for speaker identification
Loading...
Identifiers
Publication date
2021-05-10
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Impact
Export
Abstract
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.
Description
Keywords
Denoising autoencoder, Speaker embeddings, Noisy conditions, Stress, End-to-end model, Speaker identification
Bibliographic citation
Rituerto-González, E. & Peláez-Moreno, C. (2021, 10 mayo). End-to-end recurrent denoising autoencoder embeddings for speaker identification. Neural Computing and Applications, 33(21), 14429-14439.