A Machine of Few Words Interactive Speaker Recognition with Reinforcement Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2020

A Machine of Few Words Interactive Speaker Recognition with Reinforcement Learning

Abstract

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.
Fichier principal
Vignette du fichier
Interspeech_2020.pdf (641.83 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03123999 , version 1 (28-01-2021)

Identifiers

Cite

Mathieu Seurin, Florian Strub, Philippe Preux, Olivier Pietquin. A Machine of Few Words Interactive Speaker Recognition with Reinforcement Learning. Conference of the International Speech Communication Association (INTERSPEECH), Oct 2020, Shanghai, China. ⟨10.21437/Interspeech.2020-2892⟩. ⟨hal-03123999⟩
77 View
106 Download

Altmetric

Share

Gmail Facebook X LinkedIn More