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A Machine of Few Words - Interactive Speaker Recognition with Reinforcement Learning

Mathieu Seurin
Florian Strub
Philippe Preux
Olivier Pietquin
Proceedings of Interspeech (2020)


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, in contrast to the standard text-dependent or text-independent schemes, the recognition system aims at incrementally build a representation of the speakers by requesting personalized utterances to be spoken. 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 very good performance while using little speech signal amounts. This method could also be applied as an utterance selection mechanism for building speech synthesis systems.