Self-supervised deep learning approaches to speaker recognition: A Ph.D. Thesis overview

Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in prac- tice. In i-vector based speaker recognition, cosine (unsuper- vised) and PLDA (supervised) are the basic scoring techniques, with a big performance gap between the two. In this thesis we tried to fill this gap without using speaker labels in several ways. We applied Restricted Boltzmann Machine (RBM) vectors for the tasks of speaker clustering and tracking in TV broadcast shows. The experiments on AGORA database show that us- ing this approach we gain a relative improvement of 12% and 11% for speaker clustering and tracking tasks, respectively. We also applied DL techniques in order to increase the discrimina- tive power of i-vectors in speaker verification task, for which we have proposed the use of autoencoder in several ways, i.e., (1) as a pre-training for a Deep Neural Network (DNN), (2) as a near- est neighbor autoencoder for i-vectors, (3) as an average pooled nearest neighbor autoencoder. The experiments on VoxCeleb database show that we gain a relative improvement of 21%, 42% and 53%, using the three systems respectively. Finally we also proposed a self-supervised end-to-end speaker verification system. The architecture is based on a Convolutional Neural Network (CNN), trained as a siamese network with multiple branches. From the results we can see that our system shows comparable performance to a supervised baselineThis work was supported by the project PID2019-107579RBI00 / AEI / 10.13039/501100011033Peer ReviewedPostprint (published version

End-to-end text-dependent speaker verification using novel distance measures

This paper explores novel ideas in building end-to-end deep neural network (DNN) based text-dependent speaker verification (SV) system. The baseline approach consists of mapping a variable length speech segment to a fixed dimensional speaker vector by estimating the mean of hidden representations in DNN structure. The distance between two utterances is obtained by computing L2 norm between the vectors. This approach performs worse than the conventional Gaussian Mixture Model-Universal Background Model (GMM-UBM) based SV on a publicly available corpora. We believe that a degraded performance is due to the employed averaging operation, which may not capture the phonetic information of an utterance. Recent studies indicate that techniques exploiting phonetic information in addition to speaker is beneficial for this task. This paper therefore proposes to incorporate content information of the speech signal by computing distance function with linguistic units co-occuring between enrollment and test data. The whole network is optimized by employing a triplet-loss objective in an end-to-end fashion to estimate SV scores. Experiments on the RSR2015 dataset indicate that the proposed approach outperforms GMM-UBM system by 48% and 36% relative equal error rate for fixed-phrase and random-digit conditions respectively

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