IROS 2015 Paper Abstract


Paper ThCT2.1

Nakamura, Keisuke (Honda Research Institute Japan Co., Ltd.), Nakadai, Kazuhiro (Honda Research Inst. Japan Co., Ltd.)

Robot Audition Based Acoustic Event Identification Using a Bayesian Model Considering Spectral and Temporal Uncertainties

Scheduled for presentation during the Regular session "Smart Robotics Application 2" (ThCT2), Thursday, October 1, 2015, 11:20−11:35, Saal D

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sept 28 - Oct 03, 2015, Congress Center Hamburg, Hamburg, Germany

This information is tentative and subject to change. Compiled on July 20, 2019

Keywords Robot Audition, Voice, Speech Synthesis and Recognition


To analyze auditory scenes of robotsí surrounding environments, not only speeches but also non-speech sounds are important, which are spatially distributed and have different spectral and temporal characteristics. Thus, this paper investigates Acoustic Event Identification (AEI) which includes problems of localization, detection, and identification of sound sources. To achieve AEI by a robot in a real environment, we first propose to use a robot audition framework including sound source localization and separation to localize, detect, and separate acoustic events. For the identification, we propose two Bayesian models, iterative Latent Dirichlet Allocation (it-LDA) and Nested Pitman-Yor process with Uncertainty Compensation (NPY-UC). it-LDA and NPY-UC extract noise-robust soundunits and sound-words, respectively, and they consider probabilistic spectral and temporal uncertainties to robustify AEI against harsh environments such as noise and reverberation, etc. We have implemented these proposed methods using a robotembedded microphone array. The preliminary results showed 5-18 pts improvement compared to a conventional GMM method in noisy environments thanks to the Bayesian framework in consideration of uncertainties.



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