Speaker Independent Arabic Speech Recognition Using Support Vector

Machine

Ass. Prof. Dr. Hala Helmy Zayed Dr. Mohamed Ibrahim Sharawy

Supervised By

Eng. Shady Yehia El-Mashad

By

Introduction

Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

Agenda

Introduction Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

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

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

How do humans do it?

• Articulation produces sound waves which the ear conveys to the brain for processing

Acoustic Signal

How might computers do it?

Acoustic Waveform

Speech Recognition

Introduction

Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

There are two main types of speaker models:

(1) 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.

Types of Speech Recognition

(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.

Speech Recognition Usually Concern Three Types of Speech

- Speaker gender - Speaker identity - Speaker language

- Psychological conditions - Speaking style

- Environmental conditions

Factors that affect the speech signal

- Digitization: Converting analogue signal into digital representation

- Signal processing: Separating speech from background noise - Phonetics:

Variability in human speech

- Continuity: Natural speech is continuous.

Some of the difficulties related to speech recognition

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.

The Three-State Representation

Fig. Three State Speech Representation

(1) Security

(2) Education

(3) Control

(4) Diagnosis

(5) Dictation

Applications of Speech Recognition

Introduction

Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

History of Speech

Previous Research(Arabic Speech)

TitleCombined 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

SourceINFOS2008, 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 HMM ANN

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%

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 RecognizingArabic Speech

Source

The International Arab Journal of Information

Technology, Vol. 1; July-2004

International Journal of Intelligent Systems and

Technologies 1; 2 © www.waset.org 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 ModelANN HMM ANN

Type of neural network MLP

general regression neural network

(GRNN)--------------- RBF

The scope of speech

Arabic Digits Arabic Digits Arabic Digits “Saudi accented “

Arabic Digits

Performance 88% 85- 91% 93.72% 87-93 %

Introduction

Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

The Proposed System

The Proposed System 1.Recording System

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.

The Proposed System 2. Data Set

The Proposed System 2. Data Set

3. The Segmentation System

The Proposed 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

3. The Segmentation System

The Proposed System

X0 1 6 5 3 2 0 4 0 3

0 1 6 5 3 2 0 4 0 3 -

3. The Segmentation System

The Proposed 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.

The feature vector must contain information that is

- useful to identify and differentiate speech sounds - identify and differentiate between speakers

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.

There are some methods such as FFT, LPC, Real Cepstrum and MFCC.

The Proposed System

4. Feature Extraction

Mel Frequency Cepstrum Coefficients (MFCC):

The Proposed System

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.

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)

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.

The Proposed System

Support Vector Machine (SVM):

The Proposed System

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.

Introduction

Characteristics of Speech Signal

History of Speech & Previous Research

The Proposed System

Results and Conclusions

Training and TestingSupport Vector Machine (SVM)

Results and Conclusions

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 10000 samples, we divide them into: Training: 70%

Cross Validation: 15% Testing: 15%

Results and ConclusionsResults

Cross Validation Confusion Matrix of the SVM

0 1 2 3 4 5 6 7 8 9

0 100.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0.00 96.00 0.00 0.00 0.00 0.00 0.00 0.00 4.00 0.00

2 0.00 0.00 90.00 0.00 0.00 0.00 0.00 0.00 10.00 0.00

3 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00

4 4.00 0.00 0.00 0.00 94.00 0.00 0.00 0.00 2.00 0.00

5 7.00 0.00 0.00 0.00 0.00 89.00 0.00 0.00 0.00 4.00

6 0.00 0.00 0.00 0.00 0.00 0.00 92.00 0.00 0.00 8.00

7 0.00 0.00 0.00 0.00 0.00 2.00 4.00 90.00 0.00 4.00

8 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00 0.00

9 0.00 0.00 0.00 0.00 0.00 0.00 6.00 0.00 0.00 94.00

Results and ConclusionsResults

The Testing Confusion Matrix of the SVM

Output / Desired 0 1 2 3 4 5 6 7 8 9

0 255 5 0 4 0 0 6 0 0 31 0 313 0 0 1 0 0 0 6 02 2 0 122 0 0 0 0 0 0 03 2 4 0 105 0 0 1 0 2 04 0 0 0 0 135 0 0 6 0 15 0 0 0 0 0 90 3 0 1 16 4 0 0 1 0 0 83 0 0 07 0 6 2 2 1 4 0 119 2 28 0 5 0 5 0 0 6 0 95 09 0 0 0 0 0 0 0 0 0 95

Results and Conclusions

Results

Performance =

(255+313+122+105+135+90+83+119+95+95) / 1500

= 1412 / 1500 = 94.13 %

Results and Conclusions•Conclusion 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).

THANK YOU!