Projektbeschreibung
Eine Technologie zur optimierten Sprechererkennung
Die Spracherkennung ist für eine breite Palette an Anwendungen von zentraler Bedeutung. Die stetigen Weiterentwicklungen im Bereich der Datenauswertungs- und Analyseverfahren bieten Lösungen für kontinuierliche Verbesserungen in der Sprachverarbeitungsbranche. Das EU-finanzierte Projekt ETE SPEAKER zielt auf die Entwicklung eines innovativen Instruments ab, das auf der automatisierten Sprechererkennung beruht und die notwendigen Informationen isoliert, die zur Feststellung der Identität von Sprechenden in einer Sprachaufnahme notwendig sind. ETE SPEAKER wird sich auf eine umfassende Untersuchung und Ausnutzung des Potenzials tiefer neuronaler Netzwerke konzentrieren, um die sprecherspezifischen Informationen von den anderen Störvariablen zu trennen. Das Hauptziel besteht in der Einführung einer durchgängigen Lösung zur Sprecheraerkennung, die den neuesten Normen der Sprechererkennungsprüfung entsprechen.
Ziel
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).
Wissenschaftliches Gebiet
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Aufforderung zur Vorschlagseinreichung
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601 90 Brno Stred
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