The last 25 years have seen a dramatic progress in statistical methods for recognizing speech signals and for translating spoken and written language.
This lecture gives an overview of the underlying statistical methods. In particular, the lecture will focus on the remarkable fact that, for these tasks and similar tasks like handwriting recognition, the statistical approach makes use of the same four principles:
- Bayes decision rule for minimum error rate,
- Probabilistic models, e.g. Hidden Markov models or conditional random fields for handling strings of observations (like acoustic vectors for speech recognition and written words for language translation),
- training criteria and algorithms for estimating the free model parameters from large amounts of data,
- the generation or search process that generates the recognition or translation result.
Most of these methods had originally been designed for speech recognition. However, it has turned out that, with suitable modifications, the same concepts carry over to language translation and other tasks in natural language processing. This lecture will summarize the achievements and the open problems in this field.
Hermann Ney is a full professor of computer science at RWTH Aachen University in Aachen, Germany. His research interests lie in the area of statistical methods for pattern recognition and human language technology and their specific applications to speech recognition, machine translation and image object recognition. In particular, he has worked on dynamic programming and discriminative training for speech recognition, on language modelling and on phrase-based approaches to machine translation. His work has resulted in more than 500 conference and journal papers (h-index 67, estimated using Google scholar). He is a fellow of both the IEEE and of the International Speech Communication Association. In 2005, he was the recipient of the Technical Achievement Award of the IEEE Signal Processing Society. In 2010, he was awarded a senior Digiteo chair at LIMIS/CNRS in Paris, France.