Denoised Senone I-Vectors for Robust Speaker Verification

Abstract

Recently, it has been shown that senone i-vectors, whose posteriors are produced by senone deep neural networks (DNNs), outperform the conventional Gaussian mixture model (GMM) i-vectors in both speaker and language recognition tasks. The success of senone i-vectors relies on the capability of the DNN to incorporate phonetic information into the i-vector extraction process. In this paper, we argue that to apply senone i-vectors in noisy environments, it is important to robustify the phonetically discriminative acoustic features and senone posteriors estimated by the DNN. To this end, we propose a deep architecture formed by stacking a deep belief network (DBN) on top of a denoising autoencoder (DAE). After backpropagation finetuning, the network, referred to as denoising autoencoder-deep neural network (DAE-DNN), facilitates the extraction of robust phonetically-discriminitive bottleneck (BN) features and senone posteriors for i-vector extraction. We refer to the resulting ivectors as denoised BN-based senone i-vectors. Results on NIST 2012 SRE show that senone i-vectors outperform the conventional GMM i-vectors. More interestingly, the BN features are not only phonetically discriminative, results suggest that they also contain sufficient speaker information to produce BN-based senone ivectors that outperform the conventional senone i-vectors. This work also shows that DAE training is more beneficial to BN feature extraction than senone posterior estimation.

Anomaly Detection Based on Denoising Autoencoder and Variational Autoencoder with Obrid-Sensor

“…The DAE method is used to add noise to input data, compressing and decompressing it like AE (7) . AE consists of encoder and decoder layers.…”