Combined Architecture of Adaptive Beamforming and Blind Source Separation for Speech Recognition of Intelligent Service Robots

Successful speech recognition in noisy environments for intelligent robots depends on the performance of preprocessing elements employed. Even though acoustic signals are often corrupted in the high noise level environment, speech recognition systems such as the widely-used HTK do not deal with signal distortion problems. We propose an architecture that effectively combines adaptive beamforming (ABF) and blind source separation (BSS) algorithms in the spatial domain. To avoid permutation ambiguity and heavy computational complexity in the BSS system, the adaptive generalized sidelobe canceller is employed in front of the BSS system. We slightly modified the conventional convolutive mixture model of the BSS for fast processing in hardware implementations. Unlike the conventional BSS, this does not suffer from permutation ambiguity since the target angle of the front-line beamformer is fixed so it always provides enhanced and reference noise signals to the predefined two inputs of the BSS. The proposed system also reduces heavy computations in the BSS when the BSS have more than two inputs. The proposed time domain approach can be easily implemented into hardware in real-time. We evaluated the structure and assessed its performance with a DSP module. The experimental results of speech recognition test show that the proposed combined system guarantees high speech recognition rate in the noisy environment and better performance than the ABF and BSS system.