Speech detection in noisy environments by wavelet energy-based recurrent neural fuzzy network

This paper proposes a new speech detection method by recurrent neural fuzzy network in variable noise-level environments. The detection method uses wavelet energy (WE) and zero crossing rate (ZCR) as detection parameters. The WE is a new and robust parameter, and is derived using wavelet transformation. It can reduce the influences of different types of noise at different levels. With the inclusion of ZCR, we can robustly and effectively detect speech from noise with only two parameters. For detector design, a singleton-type recurrent fuzzy neural network (SRNFN) is proposed. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and the recurrent property makes them suitable for processing speech patterns with temporal characteristics. The learning ability of SRNFN helps avoid the need of empirically determining a threshold in normal detection algorithms. Experiments with different types of noises and various signal-to noise ratios (SNRs) are performed. The results show that using the WE and ZCR parameters-based SRNFN, a pretty good performance is achieved. Comparisons with another robust detection method, the refined time–frequency-based method, and other detectors have also verified the performance of the proposed method.