Opis projektu
Zoptymalizowana technologia automatycznego rozpoznawania rozmówcy
Rozpoznawanie mowy ma kluczowe znaczenie w wielu zastosowaniach. Ciągły rozwój technik wykorzystywania i analizy danych sprawia, że branża przetwarzania mowy może stale ulepszać oferowane rozwiązania. Projekt ETE SPEAKER finansowany ze środków UE ma doprowadzić do opracowania innowacyjnego narzędzia opartego na automatycznym rozpoznawaniu osoby mówiącej (ang. speaker identification, SID), które będzie wyodrębniać informacje niezbędne do określenia jej tożsamości na podstawie nagrania. Projekt ETE SPEAKER będzie koncentrować się na dogłębnym zbadaniu i wykorzystaniu potencjału głębokich sieci neuronowych do wyłuskania informacji o osobie mówiącej ze zbioru kłopotliwych zmiennych. Głównym celem projektu będzie wprowadzenie kompletnej technologii SID, zgodnej z najnowszymi normami oceny rozpoznawania mowy.
Cel
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).
Dziedzina nauki
Program(-y)
Temat(-y)
System finansowania
MSCA-IF-EF-ST - Standard EFKoordynator
601 90 Brno Stred
Czechy