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Nowadays, speech recognition has become one of the important technologies for human-computer interaction. Speech recognition is essentially a process of speech training and pattern recognition, which makes feature extraction technology particularly important. The quality of feature extraction is directly related to the accuracy of speech recognition. Dynamic feature parameters can effectively improve the accuracy of speech recognition, which makes the speech feature dynamic feature extraction has higher research value. The traditional dynamic feature extraction method is easy to generate more redundant information, resulting in low recognition accuracy. Therefore, based on a new speech feature extraction method, a method based on deep learning for speech feature extraction is proposed. Firstly, speech signal is preprocessed by pre-emphasis, windowing, filtering and endpoint detection. Then, the sliding differential cepstral feature (SDC) is extracted, which contains the voice information of the front and back frames. Finally, the feature is used as input to extract the dynamic features that represent the depth essence of speech information through the deep self-encoding neural network. The simulation results show that the dynamic features extracted by in-depth learning have better recognition performance than the original features, and have a good effect in speech recognition.
Comparison of Different Speech Feature Extraction Techniques with and without Wavelet Transform to Kannada Speech Recognition
