Intelligent Information Management, 2010, 2, 58-64
doi:10.4236/iim.2010.21008 Published Online January 2010 (
Copyright © 2010 SciRes IIM
Design of Multilingual Speech Synthesis System
1Assistant Professor, Department of Information Technology, Pondicherry Engg.,College, Pondicherry, India
2Department of Information Technology, Pondicherry Engg., College, Pondicherry, India
The main objective of this paper is to convert the written multilingual text into machine generated synthetic
speech. This paper is proposed in order to provide a complete multilingual speech synthesizer for three lan-
guages Indian English, Tamil and Telugu. The main application of TTS system is that it will be helpful for
blind and mute people that they could have the text read to them by computer. TTS system will help in re-
trieving the information from sites that contain information in different languages. It can be used in educa-
tional institutions for pronunciation teaching of different languages. We use concatenative speech synthesis
where the segments of recorded speech are concatenated to produce the desired output. We apply prosody
which makes the synthesized speech sound more like human speech. Smoothing is also done to smooth the
transition between segments in order to produce continuous output. The Optimal Coupling algorithm is en-
hanced to improve the performance of speech synthesis system.
Keywords: Prosody, Smoothing, Optimal Coupling
1. Introduction
Speech synthesis is the artificial production of human
speech. A computer system used for this purpose is call-
ed a speech synthesizer, and can be implemented in soft-
ware or hardware. A text-to-speech (TTS) system conve-
rts normal language text into speech; other systems rend-
er symbolic linguistic representations like phonetic tran-
scriptions into speech. Synthesized speech can be created
by concatenating pieces of recorded speech that are stor-
ed in a database. Systems differ in the size of the stored
speech units. The speech units can be phonemes, dipho-
nes, syllables etc. For specific usage domains, the storage
of entire words or sentences allows for high-quality output.
A text-to-speech system is composed of two parts: a
front-end and a backend. The front-end has two major
tasks. First, it converts raw text containing symbols like
numbers and abbreviations into the equivalent words.
This process is often called text normalization, Preproc-
essing, or tokenization. The front-end then assigns pho-
netic transcriptions to each word, and divides and marks
the text into prosodic units, like phrases, clauses, and
sentences. The process of assigning phonetic transcrip-
tions to words is called text-to-phoneme conversion.
Phonetic transcriptions and prosody information together
make up the symbolic linguistic representation which is
carried out by the front-end. The back-end often referred
to as the synthesizer converts the symbolic linguistic
representation into sound. Although text-to-speech sys-
tems have improved over the past few years, some
shortcomings still exist. For instance, many text-to-
speech systems are designed for only a single language.
However, there are many applications that need a system
that can provide speech synthesis of words from multiple
languages, and in particular, speech synthesis where
words from two or more languages are contained in the
same sentence. In fact when we look at newspapers we
can find many sentences with embedded foreign lan-
guage expressions or proper names. It would not be ap-
propriate to have these sentences pronounced with a
speech engine developed for a single language. The pa-
per is proposed to specifically address the issues of
building synthetic voices for the three major languages
namely English, Tamil and Telugu.
The techniques employed for synthesizing speech
from text may be broadly classified into three categories:
1) Formant-based, 2) Parameter-based and 3) Concatena-
tion-based. The three sub-categories of Concatenation-
based are 1) Unit Selection Synthesis, 2) Diphone Syn-
thesis and 3) Domain-Specific Synthesis. We use unit
selection synthesis [1] in which, the prerecorded words
are split into syllables and stored with the corresponding
syllable names and maintained in a database. Based on
the input, the syllable units are selected from the data-
base and concatenated to generate the speech.
To make the speech sound more natural Prosody and
Smoothing is applied at right places. Various techniques
available for smoothing include Spectral Smoothing,
Optimal Coupling, Waveform Interpolation, LP Tech-
niques and Pitch Synchronous Overlap Add (PSOLA) [2].
We use a technique based on Optimal Coupling algo-
The most important qualities of a speech synthesis
system are naturalness and Intelligibility. Naturalness
describes how closely the output sounds like human
speech, while intelligibility is the ease with which the
output is understood. The ideal speech synthesizer is
both natural and intelligible. Speech synthesis systems
usually try to maximize both characteristics. The tech-
nologies for generating synthetic speech waveforms are
listed below.
1) Concatenative synthesis
a) Unit selection synthesis
b) Diphone synthesis
c) Domain-specific synthesis
2) Formant synthesis
3) Articulatory synthesis
4) HMM-based synthesis
5) Sinewave synthesis
Each technology has its own strengths and weaknesses,
and the intended uses of a synthesis system will typically
determine which approach is used. In Thirukkural Text-
to-Speech Synthesis System [3], Concatenative synthesis
approach is used where natural speech is concatenated to
give the resulting speech output. It involves two phases,
namely, the offline phase and the online phase. Offline
phase includes pre-processing, segmentation and pitch
marking. Online phase includes text analysis and synthe-
sis. Here, syllables have been used as basic units. The
size of the database used is high. Attempts are to be
made to make the speech sound more natural, add emo-
tions and also to provide good synthesis for alien words.
Formant-based speech synthesizer employing demi-
syllable concatenation [4], involves identifying and ex-
tracting the formants from an actual speech signal (la-
beled to identify approximate demi-syllable areas) and
then using this information to construct demi-syllable
segments each represented by a set of filter parameters
and a source signal waveform. The basic unit being
demi-syllable requires numerous concatenation points
and hence the speech lacks from continuous flow.
In Hidden Markov Model (HMM) based speech syn-
thesis system [5] the speech waveform is generated from
HMMs themselves, and applies it to English speech syn-
thesis using the general speech synthesis architecture of
Festival Framework. Although synthesized speech has a
typical quality of “vocoded speech,” it has been shown
that the mixed excitation model based on Mixed Excita-
tion LPC Vocoder technique (MELP) and post filtering
can improve the speech quality significantly.
In Dhvani TTS system [6], they use phoneme con-
catenation technology with an attempt to cover all Indian
languages under a single framework. This system can
detect the languages and it dispatches the text to the cor-
responding phonetic synthesizer. Here, prosody is not
given much importance and also the quality of the speech
can be improved to some extent. We have proposed a
Multilingual Speech Synthesizer to overcome the draw-
backs of these systems to the extent as possible.
The paper is organized into 3 sections. In Section 2,
we discuss on the proposed system with the description
of modules. In Section 3, the results are discussed.
2. Multilingual TTS System
The TTS system comprises modules for Preprocessing,
Unicode Conversion, Segmentation, Concatenation, Pro-
sody and Smoothing. Figure 1 illustrates the overall ar-
chitecture of TTS system.
2.1. Preprocessing
The first stage of a TTS system is the pre-processing
module, called tokenization. It converts the input text
into a sequence of words and symbols to be processed by
the rest of the system. It identifies and makes decisions
on what to do with punctuation marks and other non-alp
habetic textual symbols (e.g. parentheses), identifies and
expands abbreviations, acronyms, and numbers to full-
blown orthographic strings. Each input line is scanned
and each recognized construct (word, number, symbol,
etc) is converted into an appropriate word or sequence of
words as shown in Figure 2.
Figure 1. Overall block diagram of the TTS system
Figure 2. Preprocessing
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Preprocessing includes,
1) The removal of single and double quotes from the
start and end of the token.
2) Expansion of abbreviations, acronyms, and num-
3) In the case of Tamil and Telugu input, if there is any
English word, it is replaced by an equivalent word in the
corresponding language.
2.2. Unicode Conversion
Unicode is an industry standard allowing computers to
consistently represent and manipulate text expressed in
most of the world's writing systems. In our work, we
have used Unicode conversion to accept the input text in
multiple fonts. The logic behind Unicode conversion is
to identify the font in which the given text is encoded
and applying Unicode encoding to the given font.
For Tamil, since the input may be in TAM, TAB or
TSCII, the encoding techniques for TAM, TAB and
TSCII are explored and for each and every character in
the input, Unicode encoding is applied to convert the
given text into unicode. Similarly for Telugu, the encod-
ing technique of shree font is explored and Unicode en-
coding is applied to the input. In case of multilingual
input, the text to be given is already converted to unicode
and saved as a text file containing inputs from all the
three languages. If the input is going to be in a single
language then the Unicode conversion will be carried out
internally as shown in Figure 3.
2.3. Segmentation
Speech in Indian language is based on basic sound units
which are inherently syllable units made from C, CV,
CCV, VC and CVC combinations, where C is a conso-
nant and V is a vowel. From perceptual results, it is ob-
served that from four different choices of speech units
like syllable, diphone, phone and half phone, the syllable
unit performs better than all the rest and is a better rep-
resentation for Indian languages.
In our work, for English, we segment the text into syl-
lables using the basic syllable pattern (C) (C) (C) V (C)
(C) (C) (C). Hence, the possible syllable patterns are one
2.3.1. Rules for Segmentation of English
1) If a word follows a basic syllable pattern then it is not
split into syllables.
2) If a word contains more than one syllable pattern
we frame rules based on the pronunciation like,
3) If CV is followed by CCV then split the word as
CVC and CV.
4) If CV is followed by VC then split the word as CV
and VC.
For Tamil and Telugu, the basic syllable pattern is
Figure 3. Unicode conversion module
Figure 4. Segmentation of text into syllables
Table 1. Word count for all the three languages
Language No. of Words Syllable count
English 4200 2225
Tamil 4213 1120
Telugu 4118 1950
represented in the form of (C) V (C) (C) and the possible
syllable patterns are V, CV, CVC, CVCC, VC, VCC [7]
2.3.2. Rules for Segmentation of Tamil and Telugu
1) If characters after CV pattern are of type CV then the
syllables are split as CV and CV.
2) If the CV pattern is followed by CCV then syllables
are split as CVC and CV.
3) If the CV pattern is followed by CCCV then sylla-
bles are split as CVCC and CV.
4) If the VC pattern is followed by V then syllables are
split as V and CV.
5) If the VC pattern is followed by CVC then syllables
are split as VC and CVC.
Based on these rules the text is split into syllables and
the location of the corresponding sound files for the syl-
lables are written in a text file.
2.3.3. Collection of Syllables (Speech)
After segmentation, recording is done at word level and
segmented into syllables manually. The recording is done
at a frequency of 48 KHz and the channel type is set as
Stereo. The syllable count is reduced by finding the syl-
lables which are common for Tamil and Telugu. Eg:
Consider the following words:
“Alochinchadam” in Telugu and “alosanai” in Tamil.
The syllables for these words are as follows Alochincha
dam–a/lo/chin/cha/dam Alosanai –a/lo/sa/nai. In this case,
the syllables ‘a’ and ‘lo’ are common for both the lan-
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guages. Hence these syllables are stored only once in the
database which can be accessed by both the languages
and thereby reduces the size of the database.
2.3.4. Collection of Input Text
We collected the input text from different sites
Domain: Nuclear deal
The following table gives details about the count of
the words and syllables for the three languages.
Syllables common between Tamil and Telugu: 323
2.4. Concatenation
In concatenative speech synthesis, the segments of re-
corded speech are concatenated to produce the desired
output [8]. Generally, this technique produces the most
natural-sounding synthesized speech since the number of
concatenating points is less. There are three main sub-
types of concatenative speech synthesis namely Unit
selection synthesis and Diphone synthesis. In our work,
we use unit selection synthesis in which, the prerecorded
words are split into syllables and are stored with the cor-
responding syllable names and maintained in a database.
In case of Tamil and Telugu, the syllable units are named
using English letters based on the pronunciation in the
corresponding language. For eg., the syllables in the
word are named as follows:
- veeinn
- duim
Based on the input, the syllable units are selected from
the database and concatenated using MATLAB simulator
integrated with java to create complete utterances as
shown in Figure 5.
2.5. Prosody and Smoothing
2.5.1. Prosody
Prosody is the rhythm, stress, and intonation of speech.
Prosody may reflect the emotional state of a speaker;
whether an utterance is a statement, a question, or a
command; whether the speaker is being ironic or sarcas-
tic; emphasis, contrast and focus; and other elements of
language which may not be encoded by grammar. Hence,
applying the concept of prosody to the TTS system
makes the synthesis speech sounds more like human
speech. There are several techniques used for prosody
[9,10]. We make use of the punctuation marks like ‘!’, ‘?’,
‘;’ to give stress. In this method, if the words in the input
text are followed by any of these special symbols they
are read with intonations. For this method to work, the
syllables which are to be read with intonation are main-
tained as a separate database and these syllable units are
Figure 5. Concatenation using unit selection synthesis
Figure 6. Smoothing
concatenated when intonation is to be given.
2.5.2. Smoothing
Smoothing is used to smooth the transition between
segments in order to produce continuous output as that
produced by human speech as shown in Figure 6.
There are several techniques used for smoothing like
Spectral Smoothing, Optimal Coupling, Waveform In-
terpolation, LP Techniques and Pitch Synchronous Over-
lap Add (PSOLA). Among all these techniques optimal
coupling algorithm is easy to implement at low cost.
It is common in concatenative synthesis that the boun-
daries of speech segments are fixed, but the optimal cou-
pling technique allows the boundaries to move to provide
the best fit with adjacent segments. A measure of mis-
match is tested at a number of possible segment bounda-
ries until the closest match is found. While any form of
measure may be used, for the sake of improving spectral
quality, using a spectral discontinuity measure is appro-
priate. Measures considered include melfrequency cep-
stral coefficients (MFCC) [11] and the auditory-neural
based measure (ANBM) [12]. It is not necessary to im-
plement optimal coupling to perform spectral smoothing,
but it does provide some improvement at a small cost.
We proposed a new technique for Smoothing based on
Optimal Coupling algorithm. As shown in Figure 7, ini-
tially we set the boundaries to search for the optimal
concatenating point. If the given syllable unit is the be-
ginning of the word then the boundary is set from 5n/6th
position to n where, n is the length of the syllable unit.
Otherwise the boundary is set from the beginning to
n/3rd position. Then the Hamming windows are formed
to process on the selected portions to find the concate-
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Figure 7. Smoothing using optimal coupling
nating point. The zero-crossing counts are found for all
the hamming windows and the point in the area contain-
ing the minimum count is chosen as the optimal con-
catenating point. Finally the syllables are concatenated at
the chosen points to generate the smoothened speech
3. Results and Analysis
The results are analyzed using two parameters namely
Mel-Frequency Cepstral Coefficients (MFCCs) and
Mean Opinion Score (MOS).
3.1. Mel-Frequency Cepstral Coefficient (MFCC)
Mel-frequency cepstral coefficients (MFCCs) are coeffi-
cients that collectively make up an MFC. They are de-
rived from a type of cepstral representation of the audio
clip. The difference between the cepstrum and the Mel-
Frequency Cepstrum is that in the MFC, the frequency
bands are equally spaced on the mel scale, which ap-
proximates the human auditory system's response more
closely than the linearly-spaced frequency bands used in
the normal cepstrum. This frequency warping can allow
for better representation of sound, for example, in audio
compression. In sound processing, the Mel-Frequency
Cepstrum (MFC) is a representation of the short- term
power spectrum of a sound, based on a linear cosine
transform of a log power spectrum on a nonlinear mel
scale of frequency.
MFCCs are commonly derived as follows:
1) Take the Fourier transform of (a windowed excerpt
of) a signal.
2) Map the powers of the spectrum obtained above
onto the mel scale, using triangular overlapping win-
3) Take the logs of the powers at each of the mel fre-
4) Take the discrete cosine transform of the list of mel
log powers, as if it were a signal.
5) The MFCCs are the amplitudes of the resulting
MFCCs are commonly used as features in speech rec-
ognition systems, such as the systems which can auto-
matically recognize numbers spoken into a telephone.
They are also common in speaker recognition, which
is the task of recognizing people from their voices. The
MFCC values are calculated for a set of original words
say, and the concatenated words. The Euclidean distance
between the values of P and Q are calculated using the
112 2
()( )(
pqp qp q
 
The results for English, Tamil and Telugu are tabulated
in Table 2,3 and 4 respectively.
The deviation of the concatenated words with the
original words in English, Tamil and Telugu based on
their MFCC features are plotted in Figures 8,9, and 10
respectively. The average deviation of MFCC value for
concatenated English words is 1.398, for Tamil 1.463 ,
and for Telugu 1.492 .Though smoothing technique
works well for all the three languages minimum devia-
tion is obtained for English.
3.2. Mean Opinion Score (MOS)
The Mean Opinion Score (MOS) provides a numerical
indication of the perceived quality of received media
after compression and/or transmission. The MOS is ex-
pressed as a single number in the range 1 to 5, where 1 is
lowest perceived audio quality, and 5 is the highest perceived
Figure 8. Words (English) vs deviation based on MFCC
Figure 9. Words (Tamil) vs deviation based on MFCC
Figure 10. Words (Telugu) vs deviation
Table 2. Result analysis for English using MFCC
Words Deviation from the original word
President 1.13
Agreement 1.26
Prevent 1.33
India 1.35
Critics 1.40
Sufficient 1.41
Overly 1.46
Nuclear 1.51
Lacking 1.56
Safeguards 1.57
Table 3. Result analysis for Tamil using MFCC
Table 4. Result analysis for Telugu using MFCC
Table 5. Rating of MOS
Mean opinion score (MOS)
MOS Quality Impairment
5 Excellent Imperceptible
4 Good Perceptible but not annoying
3 Fair Slightly annoying
2 Poor Annoying
1 Bad Very annoying
Table 6. Results of mean opinion score
LISTENERS English Tamil Telugu
Male (28) 3.9 3.7 3.5
Female (40) 4.0 3.9 3.6
audio quality measurement. The MOS is generated by
averaging the results of a set of standard, subjective tests
where a number of listeners rate the heard audio quality
of test sentences read aloud by both male and female
speakers over the communications medium being tested.
A listener is required to give each sentence a rating using
the rating scheme as shown in Table 5 and MOS is the
arithmetic mean of all the individual scores, and can
range from 1 (worst) to 5 (best). We took objective
measurements from listener tests. Sixty expert listeners
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were asked to indicate their preferences in terms of natu-
ralness and intelligibility for different words and phrases.
Table 6 shows the preliminary results of Mean Opinion
Score (MOS).
The listeners ranked the smoothened speech produced
after concatenation as compared with natural speech. The
results taken using these parameters signify that the
speech is generated with minimal distortion.
4. Conclusions
The system has been successfully developed for the three
languages Indian English, Tamil and Telugu. Syllable
based concatenation was used which reduced the con-
catenation points and hence minimal distortion. Optimal
Coupling technique has been used for Smoothing which
resulted in natural sounding speech. The synthesizer was
tested for its quality through expert listeners. In the pro-
posed work we process only domain restricted text. In
future the Synthesizer has to be enhanced to process un-
restricted text and the Smoothing technique needs to be
explored further to find the optimal concatenating point.
Also more Indian languages have to be included.
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