1. Indian Institute of Technology Bombay
SPEECH SYNTHESIS
Indian Institute of Technology Bombay
Department of Computer Science and Engineering
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

2. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
TEXT TO SPEECH FOR MARATHI
Synthesis Methods
1. Articulatory Synthesis Not well developed
2. Formant Synthesis Poor Quality
3. Concatenative Synthesis -- Good and mostly used method
Concatenative Synthesis
It employs : Pre - stored Speech Units
Speech Units:
1. Sentences and phrases
Usefull d in small applications like
appliance responses
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

3. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
2. Words : Limited Vocabulary
systems, used in raiway
announcements
3. Diaphones: Used in Unlimited Vocabulary TTS application
Quality : intelligible and OK but requires all diaphone
date base
4. Phoneme : Used in Unlimited vocabulary
TTS applications
Quality : Lowest language speech Unit
so more concatenative distortion but very small data base.
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

4. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
The quality is progressively lower in TTS using
lower language units. But It is challenge to make the system using (3) and (4) intelligible and reasonably good quality
Experimental Systems based on (3) and (4) are under investigation at IIT Bombay
Quality
Number of sentences
low
high
Sentences/phrases
Words and Phrasesl
Diaphone concatenation
phoneme concatenation
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

5. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
Text Analysis
Text Normalisation
Linguistic Analysis
TTS ARCHITECTURE
Tagged Text
Phonetic Analysis
Grapheme to Phoneme
Conversion
Tagged Phonemes
Prosodic Analysis
Pitch and Duration
Controls
Speech Synthesis
Voice rendering
Audio stream
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

6. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
Size of vocabulary depends on the approach Used
At diaphone level there are approx 500 basic uttarances are required to be stored
Each Unit requires approximately 6000 samples requiring 30,00000 bytes (3 MB)
(8 bit samples at 8000 samples/sec) with 4 variations becomes 12 MB
At phoneme level: Consonants are very small in duration (500 samples) taking
total size to approx 40*500 bytes= 20 K plus 12 vowels each requiring 6000 samples
72 K. Approx 100K bytes are adequate. .
It is our basic philosophy to use only one basic sample and create variants by
processing the speech signal for the requirements of pitch duration stress etc..
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

7. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
Demonstration of the TTS Employing Diaphones
The system can take any text input and produces the phonetic audio output
It is does some processing of waveform while concatenating the waveforms
to create better sound effects like decay etc.
Tags have been predefined for forming words so that duration of individual units is modified.
No sentence level prosodic has been put up.
Future Work
1. Make Rules for generating tags
Difficulty: No linguistic research available on this aspect on Marathi
2. Remove concatenative distortion by processing signals,
Should be possible to some extent.
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay

8. Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay
INDIAN INSTITUTE OF TECHNOLOGY BOMBAY
THANK YOU
Text to Speech Synthesis Prof Moreshwear R Bhujade, CSE, IIT Bombay