Discrimination algorithm using voiced detection method and time-delay neural network system by 3 FFT sub-bands
Jae Seung Choi · International Journal of Computational Vision and Robotics · 2015
In the field of speech processing, the main application of a neural network is the category discrimination of phoneme recognition and speech/speaker recognition. In a speech signal, information on the time variation is significant when training the neural network for the speech signal input. Therefore, this paper proposes a speech discrimination algorithm in noisy speech signals using a voiced detection method and a time–delay neural network with a time structure. Thus, this algorithm first detects voiced sections using the proposed neural network at each frame in the condition of background noises, then discriminates the speech signals using the time–delay neural network based on three fast Fourier transform sub–bands, in the noisy environments. The effectiveness of the proposed algorithm is experimentally confirmed based on measuring the correct discrimination rates for speech degraded by various noises.