26 research outputs found
Adversarial Speaker Adaptation
We propose a novel adversarial speaker adaptation (ASA) scheme, in which
adversarial learning is applied to regularize the distribution of deep hidden
features in a speaker-dependent (SD) deep neural network (DNN) acoustic model
to be close to that of a fixed speaker-independent (SI) DNN acoustic model
during adaptation. An additional discriminator network is introduced to
distinguish the deep features generated by the SD model from those produced by
the SI model. In ASA, with a fixed SI model as the reference, an SD model is
jointly optimized with the discriminator network to minimize the senone
classification loss, and simultaneously to mini-maximize the SI/SD
discrimination loss on the adaptation data. With ASA, a senone-discriminative
deep feature is learned in the SD model with a similar distribution to that of
the SI model. With such a regularized and adapted deep feature, the SD model
can perform improved automatic speech recognition on the target speaker's
speech. Evaluated on the Microsoft short message dictation dataset, ASA
achieves 14.4% and 7.9% relative word error rate improvements for supervised
and unsupervised adaptation, respectively, over an SI model trained from 2600
hours data, with 200 adaptation utterances per speaker.Comment: 5 pages, 2 figures, ICASSP 201
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