Publications
Auditory-Based Acoustic-Phonetic Signal Processing for Robust
Automatic Recognition of Speaker-Independent Continuous Speech
Automatic speech recognition (ASR) has been an area of intense research
for more than four decades. Despite recent success in this field, the
current state-of-the-art ASR systems are terribly deficient when compared
to the human performance. They require training when used by a new
speaker, their performance deteriorates significantly in presence of
noise or any mismatch from the training environment and they have
limited real vocabulary size (perplexity). These systems behave as a
"Black Box" with minimal integration of acoustic-phonetic,
Psychological, and auditory knowledge. Part of the deficiency in
performance is attributed to the limited amount of acoustic-phonetic
knowledge incorporated in those systems and the limited understanding of
the different sources of acoustic-phonetic variability of speech.
To help solve these problems, we investigate the acoustic-phonetic
characteristics of the continuous speech basic building units (i.e. the
phonemes). We use an auditory-based front-end processing system instead
of the traditional front-end systems. The acoustic-phonetic features
which prove to be rich in their information content are extracted and new
algorithms are designed to use these features for phoneme recognition.
Such extraction and manipulation algorithms are very simple and parallel in
nature. Using this approach in the fricative and stop consonants, which
are the most difficult phonemes in their recognition, achieved a recognition
accuracy of 90%-95% for continuous speech spoken by multiple speakers (22)
from 5 different dialect regions of American English. This represents a
significant improvement over the 75%-80% rate which was achieved before
using traditional techniques.
Using these feature extraction algorithms as a front-end for a
state-of-the-art system, which is typically a Hidden Markov Model (HMM)
system, is expected to improve robustness in presence of adverse
conditions. It will
integrate more speech knowledge in the recognition process and combine
both phoneme-level and word-level recognition. Moreover, implementing
this front-end processing in hardware, will take advantage of the parallel
nature of the processing to obtain real-time fast results.
Here is a summary file of our results in .pdf
format.

I am a PhD candidate at the Department of
Electrical Engineering, University of Pennsylvania.
My advisor is Prof. Jan Van der Spiegel, and my research topic is:
"Acoustic-Phonetic Signal Processing for Speech Recognition".
I had my M.Sc. and B.Sc. degrees (both with a grade of Distinction with
Honor) from Ain Shams University,
Cairo, Egypt. My Master thesis topic was: Analog Behavioral Modeling
and its use in Analog VLSI Design. My B.Sc. thesis topic was:
"Optical Fiber Communication Systems: Case Study and Implementation of
the Receiver Amplifier".
Before going to college, I spent my twelve
years of school in an English private school (The English Mission
College). I started learning English when I was 5 years old.
I also learnt French for 6 years and German for 2 years. On my graduation
from High School, I was the First student (Valedictorian) over all Egypt in
the General
Graduation Exam (the dreaded Thanaweyia Amma). This exam is a
graduation exam as well as a qualifying exam to enter the university. Its
score determines which university and what major the student will join.
It is a unified exam over all Egyptian schools.

1. The Egyptian Ministry of Education award of
Excellence (1986) for being
the First student over all Egypt in the High School General Graduation Exam. (Thanaweyia Amma).
2. The Cairo Governorate award of Excellence (1986) for being
the First student over all Egypt in the High School General Graduation Exam. (Thanaweyia Amma).
3. Ain Shams University award of Excellence
(1991) for being the
First student over my class for 5 consecutive years (1986-1991).
4. The Egyptian Ministry of Education award
of Excellence (1991)
for being the First student over my class in the B.Sc. degree.
5. University Fellowship from the
University of Pennsylvania, 1994-1995.
6. George Stephenson Foundation Fellowship,
1995-1996.

1. January, 1995 - present:
PhD student in the Electrical Engineering department, University of Pennsylvania. My research
involves the use of Acoustic-Phonetic Feature-Based Signal Processing
for Automatic Speech Recognition.
2. 1993-1995:
Working in developing Electrical Engineering Software CAD tools for "ANACAD
EES", Germany (now part of "Mentor Graphics").
My work involved building Analog Behavioral Models for complicated
circuits like DC-DC Converters, Operational Amplifiers, Comparators, A/D
Converters, etc. I also designed and developed
some CAD GUI software packages such as the "Automatic Model Generator".
3. Summer of 1990:
Summer training in "Schlumberger Wireline and Testing". My work involved
studying and operating the electronic equipment used for measurements and
sensing in oil wells.
4. Summers of 1988 and 1989:
Summer training in Siemens AG, Erlangen, Germany. I was involved
in the design and development of the Automatic Control Systems used
to operate state-of-the-art cement plants.
5. 1991-1994:
Assistant Lecturer/Research Assistant in the Electronics and
Communication
Engineering department, Ain Shams University, Cairo, Egypt.
6. 1986-1991:
Undergraduate Student in the Electrical Engineering department, Electronics and Communication section,
Ain Shams University, Cairo, Egypt. (5 years Program).

- Introduction to Statistical Communication.
- Networking - Technology, Protocols and Practice.
- Networking - Theory and Fundamentals.
- Transmission Systems for Telecommunication.
- Digital Signal Processing.
- Digital Signal Processing Laboratory.
- Digital Integrated Circuits and VLSI Fundamentals.
- Probability Theory and Random Processes.
- Analog Integrated Circuits.
- Mixed Analog/Digital VLSI Circuits.
- Solid State Electronics.
- Principles of Electronic Devices.
- Computer Architecture.
- Acoustic-Phonetics. (Independent Study).
- Numerical Analysis Techniques.
- High Resolution Microwave Imaging.
- Neural Networks and Applications
- Auditory Neurobiology. (Audit).
- Introduction to Artificial Intelligence. (Audit).
- Linear Systems.
- Applied Electromagnetic Theory.

- Excellent experience in programming with C/C++, Java, Pascal, HTML,
Javascript and Basic languages.
- Excellent experience in object-oriented programming using C++.
- Excellent experience in Mathematics and DSP software like Matlab and
Mathcad.
- Excellent experience in DSP and the TMS320Cxx assembly language
programming.
- Excellent experience in VLSI digital, analog and mixed-mode circuit
design.
- Excellent experience in Analog Behavioral Modeling using the VHDL,
FAS, MAST and VHDL-A langauges.
- Excellent experience with electronic circuit simulators
including HSPICE, ELDO and SABER.
- Totally familiar with FPGA systems like Xilinx and Actel systems.
- Good experience in hardware and circuit design using
microcontrollers, ICs and discrete components.
- Totally familiar with most VLSI design environments
including CADENCE, VIEWLOGIC and Mentor Graphics.
- Totally familiar with many electronic layout packages like LEDIT,
ICEDIT, OrCAD, etc.
- Totally familiar with most operating systems: DOS, UNIX (Ultrix,
HP-UX, Solaris, SunOS), CP/M and Concurrent CP/M.
- Good experience in assembly language programming for different
microprocessors.
- Good experience in automatic control languages like the Siemens S5
language.

1. A.M.Abdelatty, H.Haddara and H.F.Ragaie, "Analog
Behavioral Modeling of
Artificial Neural Networks", International Conference on Microelectronics (ICM),
Turkey, 1994.
2. A.M.Abdelatty and H. Haddara, "Automatic Generation of Analog
Behavioral Models", IEEE International Conference on Electronics, Circuits and Systems (ICECS),
1994.

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Ahmed M. Abdelatty Ali
(C) Copyright 1996-1998 by Ahmed M. Abdelatty Ali. All Rights Reserved.