| 摘要 |
A robust speech recognition system according to the present invention improves a sound source by using an MPDR beamformer in a pre-processing process, applies an HIVA learning algorithm to the composed signals of the improved sound source signals and noise signals, and extracts a feature vector of the sound source signals. The speech recognition system applies a non-holonomic constraint and a minimal distortion principle when performing the HIVA learning algorithm to minimize signal distortion and improve convergence of a non-mixing matrix. In addition, the speech recognition system checks for missing features in the learning process by using an improved sound source and a noise sound source and compensates for the same. By the aforementioned features, the robust speech recognition system provides a system resistant to noise on the basis of an independent vector analysis algorithm using harmonic frequency dependency. [Reference numerals] (200) Signal input unit;(210) Signal converting unit;(220) Pre-processing unit;(230) Sound source extracting unit;(246) Mask generating unit;(248) Loss property compensation output unit;(250) DCT converting unit;(260) Voice recognition unit;(AA,BB) Log unit |
