TITLE: SPEECH RECOGNITION AND MACHINE TRANSLATION: FROM BAYES DECISION THEORY TO MACHINE LEARNING AND DEEP NEURAL NETWORKS
Summary: The last 40 years have seen a dramatic progress in machine learning and statistical methods for speech and language processing like speech recognition, handwriting recognition and machine translation. Many of the key statistical concepts had originally been developed for speech recognition. Examples of such key concepts are the Bayes decision rule for minimum error rate and sequence-to-sequence processing using approaches like the alignment mechanism based on hidden Markov models and the attention mechanism based on neural networks.
Recently the accuracy of speech recognition, handwriting recognition machine translation could be improved significantly by the use of artificial neural networks and specific architectures, such as deep feedforward multi-layer perceptrons and recurrent neural networks, attention and transformer architectures. We will discuss these approaches in detail and how they form part of the probabilistic approach.
Additional information can be found here: https://univ-cotedazur.fr/events-uca/deep-learning-school
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