Speech signal processing has become a challenging area in speaker separation and recognition under noisy conditions. Hereafter, new researchers and areas of development have led speaker identification as a speech processing subfield. The traditional speaker recognition method uses an autocorrelation detection algorithm. When disturbed by the background noise, the detection output signal-to-noise ratio is not high. Thus, an algorithm is proposed based on wavelet speech enhancement and text-related feature extraction. The speaker speech recognition system’s overall design is done in a noisy environment by voice noise’s feature matching to complete speech signal noise filtering processing. Wavelet adaptive feature decomposition is used to accomplish speech enhancement processing and to extract the relevant features. The extracted is put into the backpropagation (BP) neural network classifier to realize speaker recognition. The simulation results show that the algorithm for speech detection and analysis has high recognition accuracy, low probability of false detection, good performance of noise reduction, and superior indicators.