A noise-robust front-end based on tree-structured filter-bank for speech recognition

This paper presents a new model of auditory preprocessors based on tree-structured filter-banks to provide the robustness to noise and the easiness of hardware implementation. The robustness to noise is further improved by two components: one is the adaptive Q control of filter-bank in the sense of enhancing the contrast between frequency channels and another is the adaptive gain control of filter-bank outputs in the sense of enhancing the contrast between time frames. As a result, the proposed approach has shown the better performance of speech recognition in noisy environment compared to other approaches of auditory preprocessors. To show the effectiveness of our approach, the simulation for the English digit recognition of TI46-Word database has been performed.