Optimization of speech endpoint detection base on sub-band energy feature

In order to detect more robustly and precisely the endpoints of speech under noisy environments, an algorithm was proposed in this paper by combining the multiple sub-bands energy as feature and optimal edge detection as decision criteria. The algorithm highlights itself by exemption of the adjustment of the decision threshold due to the stable output of the filters under the environments with different signal-to-noise ratio. Experiments showed that, while easy to tune the parameters, the algorithm can work more robustly under various noisy environments. Thus overtaking the traditional short-time energy, zero crossing rate and pitch based methods.