Simulation of Audiovisual Bimodal Speech Recognition Based on Improved Particle Filter Tracking
In noisy environments,audio-visual speech is not easily recognized.To improve speech recognition performance,an improved particle filter tracking audio-visual bimodal speech recognition method is proposed.Firstly,spectral subtraction was adopted to remove noise data,thus completing the noising removal of audiovisual dual-modal speech.Based on the correlation between human speech and lip movement information,an improved particle filter tracking method was adopted to extract audiovisual dual-modal speech feature information,and then a transformer speech recognition model was constructed.Finally,the extracted information was input into the model for parallel training,thus achieving the effective recognition for audiovisual dual-modal speech.The experimental results show that the proposed method show high feasibility and strong reliability after the signal-to-noise ratio test and recognition performance test.
Speech recognition modelSpectral subtractionNoise removalIdentification training
NETL
NSTL
万方数据
