1. Eng. Shady Yehia El-Mashad
Speaker Independent Arabic Speech Recognition Using Support Vector Machine By
Eng. Shady Yehia El-Mashad
Supervised By
Ass. Prof. Dr. Hala Helmy Zayed
Dr. Mohamed Ibrahim Sharawy

2. Agenda Introduction Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

3. Introduction Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

4. Recognition
Is one of the basic memory tasks. It involves identifying objects or events that have been encountered before. It is the easiest of the memory tasks.
It is easier to recognize something, than to come up with it on your own

5. Speech Recognition System
Also Known as Automatic Speech Recognition or Computer Speech
Automatic Speech Recognition (ASR) is the process of converting captured speech signals into the corresponding sequence of words in text.
ASR systems accomplish three basic tasks:
1- Pre-processing
2- Recognition
3-Communication

6. How do humans do it?
Articulation produces sound waves which the ear conveys to the brain for processing

7. How might computers do it?
Acoustic Signal
Acoustic Waveform
Speech Recognition

8. Characteristics of Speech Signal
Introduction
Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

9. Types of Speech Recognition
There are two main types of speaker models:
Speaker independent
Speaker independent models recognize the speech patterns of a large group of people.
(2) Speaker dependent
Speaker dependent models recognize speech patterns from only one person.

10. Speech Recognition Usually Concern Three Types of Speech
(1) Isolated Word Recognition
Is the simplest speech type because it requires the user to pause between each word.
(2) Connected Word Recognition
Is capable of analyzing a string of words spoken together, but not at normal speech rate.
(3) Connected Speech Recognition (Continuous Speech Recognition)
Allows for normal conversational speech.

11. Factors that affect the speech signal
- Speaker gender
- Speaker identity
- Speaker language
- Psychological conditions
- Speaking style
- Environmental conditions

12. Some of the difficulties related to speech recognition
- Digitization:
Converting analogue signal into digital representation
- Signal processing:
Separating speech from background noise
- Phonetics:
Variability in human speech
- Continuity:
Natural speech is continuous.

13. The Three-State Representation
Three-state representation is one way to classify events in speech.
The events of interest for the three-state representation are:
• Silence (S) - No speech is produced.
• Unvoiced (U) - Vocal cords are not vibrating, resulting in an aperiodic or random speech waveform.
• Voiced (V) - Vocal cords are vibrating periodically, resulting in a speech waveform that is quasi-periodic.

14. Fig. Three State Speech Representation

15. Applications of Speech Recognition
Security
(2) Education
(3) Control
(4) Diagnosis
(5) Dictation

16. History of Speech & Previous Research
Introduction
Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

17. History of Speech

18. Previous Research(Arabic Speech)
Title
Combined Classifier Based Arabic Speech Recognition
Comparative Analysis of Arabic Vowels using Formants and an
Automatic Speech Recognition System
HMM AUTOMATIC SPEECH RECOGNITION SYSTEM OF
ARABIC ALPHADIGITS
Phonetic Recognition
of Arabic Alphabet letters using Neural Networks
Source
INFOS2008, March 27-29, 2008 Cairo-Egypt © 2008 Faculty of Computers & Information-Cairo University
International Journal of Signal Processing, Image Processing and Pattern Recognition- Vol. 3, No. 2; June-2010
the Arabian Journal for Science and Engineering, Volume 35, Number 2C; December- 2010
International Journal of Electric & Computer Sciences IJECS-IJENS, Vol: 11, No: 01; February-2011
The technique used
ANN
HMM
Type of neural network
combined classifier
PCA
The scope of speech
6 isolated word from Holy Quran
Arabic vowels
(10 words)
Alpha Digits
“Saudi Accented “
Arabic Alphabet
Performance
93%
91.6%
76%
96%

19. Previous Research(Arabic Digits)
Title
Recognition of Spoken Arabic Digits Using Neural Predictive Hidden Markov Models
Efficient System for Speech Recognition using General Regression Neural Network
Speech Recognition System of Arabic Digits based on A Telephony Arabic Corpus
Radial Basis Functions With Wavelet Packets For Recognizing
Arabic Speech
Source
The International Arab Journal of Information Technology, Vol. 1; July-2004
International Journal of Intelligent Systems and Technologies 1; 2 © Spring 2006
Intensive Program on Computer Vision (IPCV'08), Joensuu, Finland; August-2008
CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control and signal processing; 2010
The technique used
Neural Network and Hidden Markov Model
ANN
HMM
Type of neural network
MLP
general regression neural network (GRNN)
RBF
The scope of speech
Arabic Digits
“Saudi accented “
Performance
88%
85- 91%
93.72%
87-93 %

20. The Proposed System Introduction Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

21. The Proposed System

22. The Proposed System
1.Recording System

23. The Proposed System 2. Data Set
Creating of a speech database is important for the development researcher.
For English language: we don't need to create a database because there is already more than one have been created to help the researcher on their research like sphinx1,2,3&4 and Australian English
For Arabic language, we should try to create a database that help us.

24. The Proposed System
2. Data Set

25. The Proposed System 3. The Segmentation System
Segmentation process is implemented by two techniques; semi-automatic and fully-automatic.
Semi-automatic technique:
We adopt the segmentation parameters which are window size, minimum amplitude, minimum frequency, maximum frequency, minimum silence, minimum speech, and minimum word manually by trial and error.
In this technique, we achieve only 70 percent performance, which is not very high and with this technique we can’t continue in our system because we still have two stages after that which is the feature extraction and the recognition

26. The Proposed System
3. The Segmentation System X

27. The Proposed System 3. The Segmentation System
Fully-automatic techniques
These parameters are set automatically to get better performance by using the K-Mean clustering.
By this technique we achieve nearly 100 percent in the segmentation of the digits.
The K-Means Algorithm process is as follows:
The dataset is partitioned into K clusters and the data points are randomly assigned to the clusters.
For each data point:
Calculate the distance from the data point to each cluster.
If the data point is closest to its own cluster, we leave it, and if not move it into the closest cluster.
Repeating the above step until a complete pass through all the data points resulting in no data point moving from one cluster to another.

28. The Proposed System 4. Feature Extraction
When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (much data, but not much information) then the input data will be transformed into a reduced representation set of features (also named features vector).
Transforming the input data into the set of features is called Feature extraction.
The feature vector must contain information that is
- useful to identify and differentiate speech sounds
- identify and differentiate between speakers
There are some methods such as FFT, LPC, Real Cepstrum and MFCC.

29. The Proposed System Mel Frequency Cepstrum Coefficients (MFCC):
Take the Fourier transform of a signal.
Map the powers of the spectrum obtained above onto the mel scale, using triangular overlapping windows.
Take the logs of the powers at each of the mel frequencies.
Take the discrete cosine transform of the list of mel log powers, as if it were a signal.
The MFCCs are the amplitudes of the resulting spectrum.

30. The Proposed System
5. Neural Network Classifier
There are many Neural Models, Each model has advantages and disadvantages depending on the application. According to our application we choose
Support Vector Machine (SVM)

31. The Proposed System Support Vector Machine (SVM):
A Support Vector Machine (SVM) is implemented using the kernel Adatron algorithm which constructs a hyperplane or set of hyperplanes in a high dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.

32. Support Vector Machine (SVM):
The Proposed System
Support Vector Machine (SVM):
H3 (green) doesn’t separate the two classes; H1 (blue) separates the two classes but with a small margin and H2 (red) separates the two classes with the maximum margin.

33. Results and Conclusions
Introduction
Characteristics of Speech Signal
History of Speech & Previous Research
The Proposed System
Results and Conclusions

34. Results and Conclusions
Training and Testing
Support Vector Machine (SVM)
We use the SVM network and adapting its parameter as follows:
no hidden layers.
The output layer has 10 neurons.
And we train with maximum epochs of 1000.
We have samples, we divide them into:
Training: 70%
Cross Validation: 15%
Testing: 15%

35. Results and Conclusions
Cross Validation Confusion Matrix of the SVM 1 2 3 4 5 6 7 8 9
100.00
0.00
96.00
4.00
90.00
10.00
94.00
2.00
7.00
89.00
92.00
8.00
6.00

36. Results and Conclusions
The Testing Confusion Matrix of the SVM
Output / Desired 1 2 3 4 5 6 7 8 9
255
313
122
105
135 90 83
119 95

37. Results and Conclusions
Performance =
( ) / 1500
= / = %

38. Results and Conclusions
A spoken Arabic digits recognizer is designed to investigate the process of automatic digits recognition.
The Segmentation process is implemented by two techniques; semi-automatic and fully-automatic.
The feature extracted by using MFCC technique.
This system is based on NN and by using Colloquial Egyptian dialect within a noisy environment and carried out by neuro solution tools.
The performance of the system is nearly 94% when we use (SVM).

39. THANK YOU!