Audio enhancing with DNN autoencoder for speaker recognition

• Published in: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5090 - 5094
• Main Authors: Plchot, Oldrich, Burget, Lukas, Aronowitz, Hagai, Matejka, Pavel
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.03.2016
• Subjects: Artificial neural networks; Conferences; Construction; de-reverberation; denoising; DNN; Microphones; neural networks; Noise measurement; Preprocessing; Speaker recognition; Speech; Speech enhancement; Speech processing; Speech recognition; Training
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2016.7472647

In this paper we present a design of a DNN-based autoencoder for speech enhancement and its use for speaker recognition systems for distant microphones and noisy data. We started with augmenting the Fisher database with artificially noised and reverberated data and trained the autoencoder to map noisy and reverberated speech to its clean version. We use the autoencoder as a preprocessing step in the later stage of modelling in state-of-the-art text-dependent and text-independent speaker recognition systems. We report relative improvements up to 50% for the text-dependent system and up to 48% for the text-independent one. With text-independent system, we present a more detailed analysis on various conditions of NIST SRE 2010 and PRISM suggesting that the proposed preprocessig is a promising and efficient way to build a robust speaker recognition system for distant microphone and noisy data.