Two-Microphone End-to-End Speaker Joint Identification and Localization Via Convolutional Neural Networks

We present an end-to-end scheme based on convolutional neural networks (CNNs) for speaker joint identification and localization. We investigate the possibility to estimate both the direction of arrival (DOA) and the identity of the speaker in far-field noisy and reverberant conditions using a two-channel microphone array. The proposed CNN network is designed to map the raw waveform of the two channels into the speaker identity and into the DOA of its speech signal. We analyze the identification and localization performance with simulated experiments in noisy and reverberation conditions.