Fuzzy Speech Recognition Algorithm Based on Continuous Density Hidden Markov Model and Self Organizing Feature Map

Speech recognition refers to the process of receiving and understanding human speech input through a computer, converting it into readable text or instructions. In order to improve the denoising effect and speech recognition effect of fuzzy speech, a fuzzy speech recognition algorithm based on continuous density hidden markov model and self-organizing feature map is proposed. Firstly, the conventional Wiener filtering algorithm is improved by using the dynamic estimation algorithm of noise power spectrum, and the endpoint detection of noisy speech signal is performed by using spectral entropy, and the noise power spectrum of the silent segment is dynamically updated according to the detection results to obtain a more ideal priori signal to noise ratio; Secondly, the fuzzy speech is input into the Wiener filter to eliminate the noise in the speech signal; Then, Mel-frequency cepstrum coefficient of speech signal is extracted as speech feature; Finally, combined with the continuous hidden Markov model and the self-organizing feature neural network in the artificial intelligence algorithm, through the process of adjusting parameters, Viterbi decoding, and the time adjustment of the voice signal in the same state, the speech classification and recognition are realized according to the speech characteristics. In the experiment, comparative experiments were conducted on the LibriSpeech dataset using speech recognition algorithms based on convolutional neural networks and recurrent neural networks, speech recognition algorithms based on residual networks and gated convolutional networks, speech recognition algorithms based on multi-scale mel domain feature map extraction. The experimental results show that the algorithm has good denoising performance. With the increase of added environmental noise intensity, the algorithm can maintain the Signal-to-Noise Ratio (SNR) of speech signals between 88dB-98dB; This algorithm can accurately detect the sound areas in the signal, and the endpoint detection accuracy is high; The accuracy and recall of the Continuous Density Hidden Markov Model-Self-Organizing Feature Neural Network (CDHMM-SOFM) designed in the algorithm increase with the number of iterations, and the highest levels of accuracy and recall can reach 0.89, respectively; The minimum recognition time of this algorithm is only 8.2 seconds, and the highest recognition rate can reach 98.7%; after applying this algorithm, the user’s error rate ranges from 0.0031 to 0.0084. The above results indicate that the algorithm has good application performance