Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Sound and Visual Tracking for Humanoid Robot
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Distance-based dynamic interaction of humanoid robot with multiple people
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Recognition of simultaneous speech by estimating reliability of separated signals for robot audition
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Blind source separation with parameter-free adaptive step-size method for robot audition
IEEE Transactions on Audio, Speech, and Language Processing
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Since a robot usually hears a mixture of sounds, in particular, simultaneous speech signals, it should be able to localize, separate, and recognize each speech signal. Since separated speech signals suffer from spectral distortion, normal automatic speech recognition (ASR) may fail in recognizing such distorted speech signals. Yamamoto et al. proposed using the Missing Feature Theory to mask corrupt features in ASR, and developed the automatic missing-feature-mask generation (AMG) system by using information obtained by sound source separation (SSS). Our evaluations of recognition performance of the system indicate possibilities for improving it by optimizing many of its parameters. We used genetic algorithms to optimize these parameters. Each chromosome consists of a set of parameters for SSS and AMG, and each chromosome is evaluated by recognition rate of separated sounds. We obtained an optimized sets of parameters for each distance (from 50 cm to 250 cm by 50 cm) and direction (30, 60, and 90 degree intervals) for two simultaneous speech signals. The average isolated word recognition rates ranged from 84.9% to 94.7%.