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ETE SPEAKER

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Robust End To End SPEAKER recognition based on deep learning and attention model...

Robust End To End SPEAKER recognition based on deep learning and attention models This project focuses on automatic speaker recognition (SID), the task of determining the identity of the speaker in a speech recording. Disentangling the speaker specific information from the rest of nuisance variability requires... ver más

31/01/2021

BRNO UNIVERSITY OF...

121K€

Presupuesto del proyecto: 121K€

Líder del proyecto

VYSOKE UCENI TECHNICKE V BRNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-01-31

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

BRNO UNIVERSITY OF TECHNOLOG...

120.82K€ | Lider

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Duración del proyecto: 21 meses Fecha Inicio: 2019-04-12
Fecha Fin: 2021-01-31

Líder del proyecto

VYSOKE UCENI TECHNICKE V BRNE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 121K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto This project focuses on automatic speaker recognition (SID), the task of determining the identity of the speaker in a speech recording. Disentangling the speaker specific information from the rest of nuisance variability requires complex models. Deep neural networks (DNNs) have recently showed their potential for this, as the popular x-vector learnt by a DNN. Here, we aim for end-to-end SID where the system is optimized as a whole for the target task. Despite several attempts in this line of research, many aspects still remain unexplored or not explored thoroughly. We also propose to explore recurrent approaches, suitable for dealing with temporal signals, as well as different pooling methods to obtain a fixed-length representation from a variable length input sequence of speech features. Next, we want to explore different flavors of attention mechanisms, which make the DNN to focus on relevant parts of the input, providing a way to quantify how much evidence has been collected about the speaker identity and the uncertainty of the obtained representation, which is a critical issue when making (Bayesian) decisions in SID. Finally, some other approaches such as using the raw signal (instead of features) or other advances that might arise will be also explored for SID and related tasks. To achieve our goals, we will start from theory, implement the proposed approaches and test on public SID benchmarks such as NIST SREs. The outcomes are intended to benefit both scientific community and speech processing industry. The applicant Dr. Alicia Lozano-Diez is an excellent female researcher, who has done her Ph.D. at Audias (Universidad Autonoma de Madrid, Spain), a respected research lab. The host group Speech@FIT from Brno University of Technology (Czechia) has a top-class track on speech processing research. Thus, we expect the combination of both the researcher and the host to boost the researcher career and benefit the host group (and its industrial European partners).

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