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Robust End-To-End SPEAKER recognition based on deep learning and attention models

Project description

An optimised automatic speaker recognition technology

Speech recognition is central to a broad spectrum of applications. The growing development of data exploitation and analysis techniques offers solutions for continuous improvement in the speech-processing industry. The EU-funded ETE SPEAKER project aims to develop an innovative tool based on automatic speaker recognition (SID) that isolates the necessary information to determine the identity of the speaker in a speech recording. ETE SPEAKER will focus on fully investigating and utilising the potential of deep neural networks to disentangle the speaker-specific information from the rest of nuisance variability. Its main aim is the introduction of an end-to-end SID conform to the latest Speaker Recognition Evaluation standards.


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).


Net EU contribution
€ 120 817,20
Antoninska 548/1
601 90 Brno stred

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Česko Jihovýchod Jihomoravský kraj
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00