Title :

A Machine of Few Words Interactive Speaker Recognition with Reinforcement Learning

Author(s) :

Seurin, Mathieu [Auteur]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Scool [Scool]
Strub, Florian [Auteur]
DeepMind [Paris]
Preux, Philippe [Auteur]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Sequential Learning [SEQUEL]
Scool [Scool]
Pietquin, Olivier [Auteur]
Google Research [Paris]

Conference title :

Conference of the International Speech Communication Association (INTERSPEECH)

City :

Shanghai

Country :

Chine

Start date of the conference :

2020-10-25

Book title :

Interspeech 2020 proceedings

English keyword(s) :

active speaker recognition
reinforcement learning
deep learning
iterative representation learning

HAL domain(s) :

Informatique [cs]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In this paper, we present a new paradigm for ...
Show more >Speaker recognition is a well known and studied task in the speech processing domain. It has many applications, either for security or speaker adaptation of personal devices. In this paper, we present a new paradigm for automatic speaker recognition that we call Interactive Speaker Recognition (ISR). In this paradigm, the recognition system aims to incrementally build a representation of the speakers by requesting personalized utterances to be spoken in contrast to the standard text-dependent or text-independent schemes. To do so, we cast the speaker recognition task into a sequential decision-making problem that we solve with Reinforcement Learning. Using a standard dataset, we show that our method achieves excellent performance while using little speech signal amounts. This method could also be applied as an utterance selection mechanism for building speech synthesis systems.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL