Deep Learning Algorithm for Speech Recognition Multiplexer System Suitable for World Congress Discussion
The difficulties encountered in building an intelligent speech recognition system and identifying various accents in speeches has been examined by this research. The research has adopted the MFCC extraction techniques using the energy values in the spectrogram generated by the neural algorithm. The sampling procedures ensured that 1/16000 wave amplitude of a second intervals were enough sample size for speech to be recognized. The deep learning neural network architecture is of 5- 9-6-3 configuration coded in python functional programming language with 250epoch runs, while the back propagation method of iteration is used to ensure that the errors are brought to the barest minimum, with average value of about 0.002 0r 0.2% which is okay for training model. The system as a whole is designed as a multiline multiplexer suitable for holding international congress meetings. The MFCC extraction techniques showed that the energy values can be used by the neurons to recognize the usable pitch in a complex sound clips
