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

Denoised senone I-Vectors for robust speaker verification

2017-2018 > Academic research: refereed > Publication in refereed journal201808 bcrcAccepted ManuscriptRGCPublishe

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Senone I-vectors for robust speaker verification

10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016, Tianjin, China, 17-20 October 2016Recent research has shown that using senone posteriors for i-vector extraction can achieve outstanding performance. In this paper, we extend this idea to robust speaker verification by constructing a deep neural network (DNN) comprising a deep belief network (DBN) stacked on top of a denoising autoencoder (DAE). The proposed method addresses noise robustness in two perspectives: (1) denoising the MFCC vectors through the DAE and (2) extracting noise robust bottleneck (BN) features and senone posteriors from the DBN for total-variability matrix training and i-vector extraction. The DAE comprises several layers of restricted Boltzmann machines (RBM), which are trained to minimize the mean squared error between the denoised and clean MFCCs. After training the DAE, three layers of RBMs are put on top of it to form the DNN. The whole network is fine-tuned by backpropagation to minimize the cross-entropy between the senone labels and network outputs. This architecture allows us to extract BN features and estimates senone posteriors given noisy MFCCs as input, resulting in robust BN-based senone i-vectors. Results on NIST 2012 SRE show that these senone i-vectors outperform the conventional i-vectors and the BN-based i-vectors in which the posteriors are obtained from a GMM.Department of Electronic and Information Engineering2016-2017 > Academic research: refereed > Refereed conference paperbcw