Abstract
In this paper we propose an embodied approach to automatic speech recognition, where a humanoid robot adjusts its orientation to the angle that increases the signal-to-noise ratio of speech. In other words, the robot turns its face to ’hear’ the speaker better, similar to what people with auditory deficiencies do. The robot tracks a speaker with a binaural sound source localisation system (SSL) that uses spiking neural networks to model relevant areas in the mammalian auditory pathway for SSL. The accuracy of speech recognition is doubled when the robot orients towards the speaker in an optimal angle and listens only through one ear instead of averaging the input from both ears.
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Dávila-Chacón, J., Twiefel, J., Liu, J., Wermter, S. (2014). Improving Humanoid Robot Speech Recognition with Sound Source Localisation. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_78
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DOI: https://doi.org/10.1007/978-3-319-11179-7_78
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