Speech dynamics and recurrent neural networks

Recently, connectionist models have been recognized as an interesting alternative tool to hidden Markov models for speech recognition. Their main property lies in their combination of good discriminating power and the ability to capture input-output relations. They have also been proved useful in dealing with statistical data. However, the serial aspect remains difficult to handle in that kind of model, and several authors have proposed original architectures to deal with this problem. This study establishes links among them and compares their respective advantages. Relations with hidden Markov models are explained