Keyword-based speaker localization: Localizing a target speaker in a multi-speaker environment

Sunit Sivasankaran 1 Emmanuel Vincent 1 Dominique Fohr 1
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Speaker localization is a hard task, especially in adverse environmental conditions involving reverberation and noise. In this work we introduce the new task of localizing the speaker who uttered a given keyword, e.g., the wake-up word of a distant-microphone voice command system, in the presence of overlapping speech. We employ a convolutional neural network based localization system and investigate multiple identifiers as additional inputs to the system in order to characterize this speaker. We conduct experiments using ground truth identifiers which are obtained assuming the availability of clean speech and also in realistic conditions where the identifiers are computed from the corrupted speech. We find that the identifier consisting of the ground truth time-frequency mask corresponding to the target speaker provides the best localization performance and we propose methods to estimate such a mask in adverse reverberant and noisy conditions using the considered keyword.
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Sunit Sivasankaran, Emmanuel Vincent, Dominique Fohr. Keyword-based speaker localization: Localizing a target speaker in a multi-speaker environment. Interspeech 2018 - 19th Annual Conference of the International Speech Communication Association, Sep 2018, Hyderabad, India. ⟨hal-01817519⟩

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