1,106 research outputs found
Adversarial Network Bottleneck Features for Noise Robust Speaker Verification
In this paper, we propose a noise robust bottleneck feature representation
which is generated by an adversarial network (AN). The AN includes two cascade
connected networks, an encoding network (EN) and a discriminative network (DN).
Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used
as input to the EN and the output of the EN is used as the noise robust
feature. The EN and DN are trained in turn, namely, when training the DN, noise
types are selected as the training labels and when training the EN, all labels
are set as the same, i.e., the clean speech label, which aims to make the AN
features invariant to noise and thus achieve noise robustness. We evaluate the
performance of the proposed feature on a Gaussian Mixture Model-Universal
Background Model based speaker verification system, and make comparison to MFCC
features of speech enhanced by short-time spectral amplitude minimum mean
square error (STSA-MMSE) and deep neural network-based speech enhancement
(DNN-SE) methods. Experimental results on the RSR2015 database show that the
proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE
and DNN-SE based MFCCs for different noise types and signal-to-noise ratios.
Furthermore, the AN-BN feature is able to improve the speaker verification
performance under the clean condition
Decision Directed Channel Estimation Aided OFDM Employing Sample-Spaced and Fractionally-Spaced CIR Estimators
Abstract—In this letter we characterize the substantial difference between two channel estimation approaches, namely the sample-spaced (SS) and the fractionally-spaced (FS) channel impulse response (CIR) estimators. The achievable performance of decision-directed channel estimation (DDCE) methods employing both the SS- and the FS-CIR estimators is analyzed in the context of an OFDM system. The performance of the two estimation methods is compared and it is shown that the DDCE scheme employing the Projection Approximation Subspace Tracking (PAST)-aided FS-CIR estimator outperforms its SS-CIR estimator-based counterpart. Index Terms—Multiuser OFDM, decision directed channel estimation, impulse response estimation SDMA
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