Theoretically, why can we get better results than traditional speech signal processing with artificial neural network?

It is parallel, self-learning and self-organizing.

1, parallelism: the traditional calculation method is developed based on the idea of serial processing, and there are a lot of interconnections between neurons in artificial neural networks. This parallel structure enables information to be quickly transmitted to each neuron for parallel processing, which improves the operation efficiency. In speech signal processing, by using parallelism, a large amount of data can be analyzed and processed simultaneously in a short time.

2. Self-learning and self-organization: The artificial neural network system has strong self-learning ability. Through the training and feedback mechanism, the weights and parameters can be continuously adjusted according to the input data, and the model can be optimized to adapt to specific tasks or problems. In speech signal processing, artificial neural network can dynamically adjust its internal representation, feature extraction and classification according to the actual situation.