1. Speech Recognition Christian Schulze
Design of a speech recognition system which distinguishes the figures 0 to 9 and the words yes/no
Applications: - speech input of telephone numbers for cellular phones (necessary in cars) - announcement of the different floors in the elevator

2. Problem Storage of all patterns requires too much memory
Algorithm which compares respective words with all stored patterns requires much calculation power => too costly and too expensive
Instead of storing the whole signal storage of representative features of the signal
=> One possiblity: formants

3. What are formants?
Speech consists of different tones which are combined with each other
Every tone has a special spectrum in the frequency domain
The maxima of the contour of the spectrum are called formants
Every tone has its own representative formants (especially vowels)

5. Data collection (98 X 1) vector used as
Recording of 50 analog samples per word
Division of the signal into parts
of 10 ms length
Calculation of the spectrum using
Discrete Fourier Transformation
figure 8 (500 ms)
Storage of the first two maxima
=> 2-Formants-Recognition-System
Smoothing of the spectrum
using Cepstral Algorithm
(98 X 1) vector
used as
input vector for training of an
MLP-network
Assign the signal
into 1 of 12 classes

6. Network and results MLP using back propagation algorithm
3 hidden layers, each with 12 hidden neurons
Learning rate=0.01, Momentum=0.1
epochs
So far best solution: learning success rate = 86.11% testing success rate = 61,67% => has to be improved upon