BRANA is an Amharic speech recognition system for dictation in the judicial domain developed by Bantegize Addis and advised by Solomon Teferra. It uses a Sphinx-4 based speech recognizer with acoustic and language models trained on a 90 minute spontaneous speech corpus and 20 hours of read speech corpus. The system achieved 84.87% accuracy on continuous read speech recognition. Future work will focus on improving performance, adding command and control features, and developing a larger spontaneous speech corpus.

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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8. 4. The Architecture of BRANA
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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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11. Implementation…(Cont.)
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Push button to initiate recogntion
GUI snapshot of BRANA(ብራና)

12. Implementation…(Cont.)
Python program implementation
 Pronunciation Dictionary
Python script program is implemented for generating a
grapheme-based canonical pronunciation dictionary
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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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17. Thank You!
Questions?
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