Filtered Dynamics and Fractal Dimensions for Noisy Speech Recognition
We explore methods from fractals and dynamical systems theory for robust processing and recognition of noisy speech. A speech signal is embedded in a multidimensional phase-space and is subsequently filtered exploiting aspects of its unfolded dynamics. Invariant measures (fractal dimensions) of the filtered signal are used as features in automatic speech recognition (ASR). We evaluate the new proposed features as well as the previously proposed multiscale fractal dimension via ASR experiments on the Aurora 2 database. The conducted experiments demonstrate relative improved word accuracy for the fractal features, especially at lower signal-to-noise ratio, when they are combined with the mel-frequency cepstral coefficients