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1. BRANA (ብራና): APPLICATION OF AMHARIC SPEECH RCOGNITION SYSTEM
FOR
DICTATION IN JUDICIAL DOMAIN
Presenter: Bantegize Addis
Adviser: Solomon Teferra
June 05, 2015
2. What I’m going to talk about…
1. Introduction…
2. Related Works….
3. Speech database….
4. Architecture…
5. Implementation…
6. Our Results….
7. Conclusion & future works…
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3. 1. Introduction
General Background
Automatic speech recognition gives us a new channel for
communication with computers.
Speech technology is the technology of today and
tomorrow.
It has practical implementations for both fun and serious
works.
Mostly applied in command and control, data entry
and retrieval, and dictation functions.
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4. Intro.(Cont.)
Statement of the Problem
Dictation has been a common application area for ASRS for a
long period.
Amharic is the second most-spoken Semitic language in the
world .
It is the official working language of the FDRE.
To the best of our knowledge, there is no attempted works
about application of speech recognition for dictation in
Amharic.
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5. 2. Related works
Automatic speech recognition for Amharic
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Author Type of
recognizer
Unit of
recognition
Recognition result
Solomon
Berhanu
Isolated Consonant-Vowel
(CV) Syllable
Speaker dependent : 87.68%
Speaker independent: 72.75%
Speaker not involved in the
training: 49.21%
Kinfe
Taddesse
Isolated Phoneme
Tri-phone
Consonant-Vowel
(CV) Syllable
Speaker independent tri-phone
(Test set I): 91.46 %
Speaker independent tri-phone
(Test set I): 77.87%
6. Related…(Cont.)
Author Type of
Recognizer
Unit of
Recognition
Recognition result
Solomon
Teferra
Continuous Tri-phone
Consonant-Vowel
(CV) Syllable
Speaker Independent tri-
phone: 91.31%
Speaker Independent CV: of
90.43%
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Application of Amharic Speech Recognition
Martha developed Amharic speech input interface to command and control
Microsoft Word
7. 3. Speech Corpus & annotation
Two types of Speech Corpus
I. Spontaneous speech corpus
II. Read speech corpus
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9. 5. Implementation of BRANA
Development Tools
JDK jdk1.7.0_05 with Eclipse
Sphinx-4
Sphinx Trainer
SRILM
cmuclmtk-0.7-win32
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10. Implementation…(Cont.)
User Interface Implementation
The user interface of our system is responsible for:
editing rtf text documents
PPT(Push To Talk) functionality
displaying the uttered word or sentence hypothesis to the
user
Developed using Java
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13. 6. Our results…
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Recognition performance of spontaneous speech recognizer
(In a Batch Recognition Mode)
Acoustic
Model
Language
Model
Accuracy
(%)
WER (%)
AM-CD_8
LM-ABS 49.537 55.093
LM-GT 49.769 56.019
LM-MKN 50.463 54.861
LM-WB 49.769 55.093
Spontaneous
Speech Recognizer
14. result…(Cont.)
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Recognition performance of continuous read speech recognizer
(In a Batch Recognition Mode)
Acoustic
Model
Language
Model
Accuracy
(%)
WER (%)
AM-CD_8
LM-ABS 84.867 16.134
LM-GT 77.227 24.310
LM-MKN 84.306 16.768
LM-WB 84.550 16.475
Continuous Read
Speech Recognizer
15. 7. CONCLUSION AND FUTURE WORKS
Conclusion
Amharic speech recognition application for dictation
BRANA
Main components : speech recognizer engine module and dictation
application module
Sub modules of speech recognizer : Acoustic model module, language
model module and pronunciation dictionary module
Front end user Interface
90 min spontaneous speech corpus and 20 hours read speech corpus
HMM approach
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16. Concl…(Cont.)
Future Works…
Automatic Error Correction
Improve performance of speech recognizer
Incorporate command and control
Noise canceling
Generic dictator
Spontaneous speech corpus
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