Rituerto González, EstherPeláez Moreno, Carmen2022-10-102022-10-102021-05-10Rituerto-González, E. & Peláez-Moreno, C. (2021, 10 mayo). End-to-end recurrent denoising autoencoder embeddings for speaker identification. Neural Computing and Applications, 33(21), 14429-14439.0941-0643https://hdl.handle.net/10016/35865Speech -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.11eng© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021Denoising autoencoderSpeaker embeddingsNoisy conditionsStressEnd-to-end modelSpeaker identificationresearch articleTelecomunicacioneshttps://doi.org/10.1007/s00521-021-06083-7open access1442914439Neural Computing & Applications33AR/0000030589