The Statistical Approach to Speech Recognition and Natural Language Processing: Achievements and Open Problems

======================================================================
                Joint Seminar
======================================================================
The Hong Kong University of Science & Technology
Human Language Technology Center
Department of Computer Science and Engineering
Department of Electronic and Computer Engineering
---------------------------------------------------------------------

Speaker:        Prof. Hermann NEY
                RWTH Aachen University, Aachen
                DIGITEO Chair, LIMSI-CNRS, Paris

Title:          "The Statistical Approach to Speech Recognition and Natural
                Language Processing: Achievements and Open Problems"

Date:           Tuesday, 4 December 2012

Time:           2:00pm - 3:00pm

Venue:          Lecture Theater H (near lifts 27 & 28), HKUST

Abstract:

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:

1) Bayes decision rule for minimum error rate; 2) 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); 3) training criteria and
algorithms for estimating the free model parameters from large amounts of
data; 4) 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.


************************
Biography:

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 68, 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.