It remains challenging for robots to accurately per-form sound source localization and speech recognition in a real environment with reverberation, noise, and the voices of multiple speakers. Accordingly, we propose “U-TasNet-Beam,” a speech extraction method for extracting only the target speaker’s voice from all ambient sounds in a real environment. U-TasNet-Beam is a neural beamformer comprising three ele-ments: a neural network for removing reverberation and noise, a second neural network for separating the voices of multiple speakers, and a minimum variance distortionless response (MVDR) beamformer. Experiments with simulated data and recorded data show that the proposed U-TasNet-Beam can improve the accuracy of sound source localization and speech recognition in robots compared to the conventional methods in a noisy, reverberant, and multi-speaker environ-ment. In addition, we propose the spatial correlation matrix loss (SCM loss) as a loss function for the neural network learning the spatial information of the sound. By using the SCM loss, we can improve the speech extraction performance of the neural beamformer.
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）