Evaluation of Bangla word recognition performance using acoustic features

Abstract

In this paper, we have prepared a medium size Bangla speech corpus and compare performances of different acoustic features for Bangla word recognition. Most of the Bangla automatic speech recognition (ASR) system uses a small number of speakers, but 40 speakers selected from a wide area of Bangladesh, where Bangla is used as a native language, are involved here. In the experiments, mel-frequency cepstral coefficients (MFCCs) and local features (LFs) are inputted to the hidden Markov model (HMM) based classifiers for obtaining word recognition performance. From the experiments, it is shown that MFCC-based method of 39 dimensions provides a higher word correct rate (WCR) than the other methods investigated. Moreover, a higher WCR is obtained by the MFCC39-based method with fewer mixture components in the HMM.

Full text

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Manuscript received September 5, 2010
Manuscript revised September 20, 2010
Performance Evaluation of Performance Evaluation of Bangla Word Recognition
Different Acoustic Features Different Acoustic Features
Bangla Word Recognition Using Using
Nusrat Jahan Lisa*1, Qamrun Nahar Eity*2, Ghulam Muhammad$
Dr. Mohammad Nurul Huda#1, Prof. Dr. Chowdhury Mofizur Rahman#2

* Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST)
$Department of CE College of CIS, King Saud University Riyadh, Kingdom of Saudi Arabia
# Department of Computer Science and Engineering United International University

Abstract— This paper describes a medium size Bangla speech
corpus preparation and the comparison of the performances of
different acoustic features for Bangla word recognition. A small
number of speakers are use for most of the Bangla automatic
speech recognition (ASR) system, but 40 speakers selected from a
wide area of Bangladesh, where Bangla is used as a native
language, are involved here. In the experiments, mel-frequency
cepstral coefficients (MFCCs) and local features (LFs) are
inputted the hidden Markov model (HMM) based classifiers for
obtaining word recognition performance. From the experiments, it
is shown that MFCC based method of 39 dimensions provides a
higher word correct rate (WCR) than the other methods
investigated. Moreover, a higher WCR is obtained by the
MFCC39-based method with fewer mixture components in the
HMM.
Keywords—mel-frequency cepstral coefficients, local
features, hidden Markov model, automatic speech
recognition, acoustic features

I. INTRODUCTION
Bangla (can also be termed as Bengali), which is largely
spoken by the people all over the world, has been
performed a very little research where many literatures in
automatic speech recognition (ASR) systems are available
for almost all the major spoken languages in the world.
About 220 million or above people speak in Bangla as their
native language. It is ranked seventh based on the number
of speakers [1]. The lack of proper speech corpus is the
major difficulty to research in Bangla ASR. Some efforts
are made to develop Bangla speech corpus to build a
Bangla text to speech system [2]. However, this effort is a
part of developing speech databases for Indian Languages,
where Bangla is one of the parts and it is spoken in the
eastern area of India (West Bengal and Kolkata as its
capital). But most of the natives of Bangla(more than two
thirds) reside in Bangladesh, where it is the official
language. Although the written characters of Standard
Bangla in both the countries are same, there are some
sound that are produced
pronunciations of Standard Bangla, in addition to the
myriad of phonological variations in non-standard dialects
[3]. Therefore, there is a need to do research on the main
stream of Bangla, which is spoken in Bangladesh, ASR.
variably in different
Bangla ASR or Bangla speech processing research can be
found in [4]-[11]. For
characterization is done in [4]; isolated and continuous
Bangla speech recognition on a small dataset using hidden
Markov models (HMMs) is described in [5]; recognition of
Bangla phonemes by Artificial Neural Network (ANN) is
reported in [8]-[9]. Continuous Bangla speech recognition
system is developed in [10], while [11] presents a brief
overview of Bangla speech synthesis and recognition.
However, most of these works are mainly concentrated on
simple recognition task on a very small database, or simply
on the frequency distributions of different vowels and
consonants.
We build an ASR system for Bangla word in a large scale
for this study. We first develop a medium size (compared
to the exiting size in Bangla ASR literature) Bangla speech
corpus comprises of native speakers covering almost all the
major cities of Bangladesh to achieve the goal. Then,
melfrequency cepstral coefficients (MFCCs) and local
features (LFs) are extracted from the input speech, then
extracted features are inserted into MLN and finally the
output of MLN are inserted into the hidden Markov model
(HMM) based classifier for obtaining the word recognition
performance. We have designed three experiments for
evaluating Bangla word correct rate (WCR), (a)
LF25+HMM, (b) MFCC38+HMM
MFCC39+HMM.
The paper is arranged as follows. Section II briefly explains
approximate Bangla phonemes with its corresponding
phonetic symbols; Section III discusses about Bangla
speech corpus; Section IV provides a brief description
about MFCC-based and LF-based methods, while Section
V describes experimental setup. Section VI explicates the
experimental results and discussion, and finally, Section
VII draws some conclusions and remarks on the future
works.

II. PHONETIC SYMBOLS FOR BANGLA
PHONEMES
example, Bangla vowel
and (c)
Table I shows Bangla vowel phonemes with their
corresponding International Phonetic Alphabet (IPA) and

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my proposed symbols. Bangla phonetic inventory consists
of 8 short vowels (A, Av, B, D, G, H, I, J), excluding long
vowels (C, E) and 29 consonants. On the other hand, the
consonants, which are used in Bangla language, are
presented in Table III. Here, the Table exhibits the same
items for consonants like as Table I. In the Table III, the
pronunciation of /k/, /l/ and /m/ are same by considering
the words ‡Kk , ‡gl
respectively, which is shown in Fig. 1. Here the meaning of
‡Kk, ‡gl and ‡Vm are English language ―hair‖, ―sheep‖
and ―an insinuating remark‖ respectively. On the other
hand, in the words Rvb
difference of pronunciation of /R/ and /h/ respectively that
depicted in Fig. 2. Here the meaning of Rvb and hvb are
English language ―life‖ and ―vehicle‖ respectively. Again,
Fig. 3 shows that there is no difference of /Y/ and /b/ in the
words cY (/pn/) and gb (/mn/) respectively. Here the
meaning of cY and gb are English language ―promise‖ and
―mind‖ respectively. Moreover, phonemes /o/ and /p/ carry
same pronunciation in the words Nvo
respectively, which is shown in the Fig. 4.
Initial consonant cluster is not allowed in the native
Bangla: the maximum syllable structure is CVC (i.e. one
vowel flanked by a consonant on each side) [12]. Sanskrit
words borrowed into Bangla possess a wide range of
clusters, expanding the maximum syllable structure to
CCCVC. English or other foreign borrowings add even
more cluster types into the Bangla inventory.

and ‡Vm
and hvb , there is no
and Mvp

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III. BANGLA SPEECH CORPUS
Lack of proper Bangla speech corpus is the main problem
to do experiment on Bangla word ASR. In fact, such a
corpus is not available or at least not referenced in any of
the existing literature. Therefore, we develop a medium
size Bangla speech corpus, which is described below. From
the Bengali newspaper ―Prothom Alo‖ [13] hundred
sentences are uttered by 30 speakers of different regions of
Bangladesh. These sentences (30x100) are used for training
corpus (D1). On the other hand, different 100 isolated
words from the same newspaper uttered by 10 different
female speakers (total 1000 isolated words) are used as test
corpus (D2). All of the speakers are Bangladeshi nationals
and native speakers of Bangla. The age of the speakers
ranges from 20 to 40 years. We have chosen the speakers
from a wide area of Bangladesh: Dhaka (central region),
Comilla – Noakhali (East region), Rajshahi (West region),
Dinajpur – Rangpur (North-West region), Khulna (South-
West region), Mymensingh and Sylhet (North-East region).
Though all of them speak in standard Bangla, they are not
free from their regional accent.
Recording was done in a quiet room located at Ahsanullah
University of Science and Technology (AUST), Dhaka,
Bangladesh. A desktop was used to record the voices using
a head mounted close-talking microphone. We record the
voice in a place, where ceiling fan and air conditioner were
switched on and some low level street or corridor noise
could be heard.
Jet Audio 7.1.1.3101 software was used to record the
voices. The speech was sampled at 16 kHz and quantized
to 16 bit stereo coding without any compression and no
filter is used on the recorded voice.

IV. SYSTEM CONFIGURATIONS
A. MFCC-based methods
Traditional approach of ASR systems uses MFCC of 39
dimensions (12-MFCC, 12-ΔMFCC, 12-ΔΔMFCC, P, ΔP
and ΔΔP, where P stands for raw energy of the input
speech signal) as feature vector to be fed into a HMM-
based classifier and the system diagram is shown in Fig. 5.
Parameters (mean and diagonal covariance of hidden
Markov model of each phoneme) are estimated, from
MFCC training data, using Baum-Welch algorithm. For
different mixture components, training data are clustered
using the K-mean algorithm. During recognition phase, a
most likely word for an input utterance is obtained using
the Forward algorithm. input utterance is obtained using
the Forward algorithm. Another system based on MFCC of
38 dimensions (12-MFCC, 12-ΔMFCC, 12-ΔΔMFCC, P,
ΔP and ΔΔP, where P stands for raw energy of the input
speech signal) was designed.

B. LF-based method
At first input speech is converted into LFs at an acoustic
feature extraction stage that represents a variation in
spectrum along time and frequency axes [14]. Two LFs are
first extracted by applying three-point linear regression
(LR) along the time t and frequency f axes on a time
spectrum pattern respectively. After compressing these two
LFs with 24 dimensions into LFs with 12 dimensions using
discrete cosine transform (DCT), a 25-dimensional (12 Δt,
12 Δf and ΔP, where P stands for log power of raw speech
signal) feature vector named LF is extracted. Then, the
extracted LFs are inserted into the HMM-based classifier
for obtaining the output word. The procedure is shown in
Fig. 6.



V. EXPERIMENTAL SETUP
MFCC comprised of 38 and 39 dimensional. Acoustic
feature vector LFs are a 25-dimensional vector consisting
of 12 delta coefficients along time axis, 12 delta
coefficients along frequency axis, and delta coefficient of

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log power of a raw speech signal [14]. The frame length
and frame rate are set to 25 ms and 10 ms, respectively, to
obtain acoustic features (MFCCs or LFs) from an input
speech.

For designing a word recognizer, WCR for D2 data set are
evaluated using an HMM-based classifier. The D1 data set
is used to design 39 Bangla monophone (8 vowels, 29
consonants, sp, sil) HMMs with five states, three loops, and
left-to-right models. Input features for the classifier are 38
dimensional MFCC and 39 dimensional MFCC, and 25
dimensional LF for the MFCC-based and LF-based
systems, respectively. In the HMMs, the output
probabilities are represented in the form of Gaussian
mixtures, and diagonal matrices are used. The mixture
components are set to 1, 2, 4, 8, 16 and 32. To obtain the
WCR we have designed the following experiments


(a) LF25+HMM
(b) MFCC38 +HMM
(c)MFCC39 +HMM

VI. EXPERIMENTAL RESULTS AND DISCUSSION
The comparison of WCR of test data set among LF25
+HMM, MFCC38+HMM and MFCC39+HMM systems is
Shown in Table III. It is observed from the table that
MFCC39-based system always provides higher WCR than
the other method investigated. For an example, at mixture
component 32, the MFCC39-based system exhibits 89.47%
correct rate, while 78.91% and 85.55% WCRs are obtained
by the methods LF25 +HMM and MFCC38 +HMM
respectively.

TABLE IV
WORD CORRECT RATE FOR INVESTIGATED METHODS


Fig. 7 shows the comparison between MFCC38 and
MFCC39. It is observed from the figure that, MFCC39
always exhibits lower word error rate over the method
based on MFCC38. The reason for providing better result
by MFCC39-based system is ΔΔP, where P stands for log
power of raw speech signal along time axis.
Moreover, from the Table IV it is noted that MFCC39
requires fewer mixture components to obtain the
approximately same numerical figure of correct rate
provided by the method based on LF25 and MFCC38. For
an example, Table V is given to indicate the computation
time more specifically with the methods based on MFCC38
and MFCC39. We have measured the HMM time required
by MFCC38 and MFCC39 by the formula mS2T where m,
S and T indicates number of mixture components, states
and observation sequences respectively. For MFCC38, the
required time is 32x52x200 (=160K), while the
corresponding time for the MFCC39 is 8x52x200 (=40K)
assuming number of observation sequence is 200 frames.
Therefore, MFCC39 based method is faster than the
method based on MFCC38.

TABLE V
Comparison of Time Complexity Between MFCC38 and MFCC39 Based
Methods

Fig. 7 Comparison between MFCC38 and MFCC39 based methods

VII. CONCLUSIONS
This paper compared performance of different acoustic
features for Bangla word recognition and performed some
experiments to obtain word recognition performance. A
higher Bangla word correct rate for test data is also
obtained by the MFCC39-based system. The author would
like to do further experiments for obtaining Bangla word
recognition performance after inserting all features into the
neural network based systems.

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thesis, BRAC
Authors Profile:
Nusrat Jahan Lisa Received B.Sc. in
Computer Science
degree from Ahsanullah University of
Science and Technology (AUST) in 2007
and she is doing M.Sc. in Computer
Science and Engineering in the United
International University, Bangladesh .
Currently She is the Lecturer of the
Department of Computer Science and
Engineering at the Ahsanullah University
and Engineering
of Science and Technology (AUST), Dhaka, Bangladesh.

Qamrun Nahar Eity Received B.Sc. in
Computer Science and Engineering degree
from Ahsanullah University of Science
and Technology (AUST) in 2008 and she
is doing M.Sc. in Computer Science and
Engineering in the United International
University, Bangladesh. Currently she is
the Lecturer of the Department of
Computer Science and Engineering at the
Ahsanullah University of Science and Technology (AUST),
Dhaka, Bangladesh.

Ghulam Muhammad received Ph.D.
Electronics and Information Engineering,
Toyohashi University of Technology,
Japan in March 2006. Currently he is the
Assistant
Computer
Computer and Information Sciences
(CCIS), King Saud University (KSU),
Riyadh, Saudi Arabia.

Dr. Mohammad Nurul Huda received
Ph.D. (Electronics
Engineering, Toyohashi University of
Technology,Japan) in 2008. Currently.he
is the Associate Professor CSE, United
International
Bangladesh.



Prof. Dr. Chowdhury Mofizur Rahman
received Ph.D. from Department of
Computer Science, Tokyo Institute of
Technology, Japan in 1996. Currently he
is the Pro-Vice Chancellor, United
International
Bangladesh.
Professor,
Engineering,
Department
College
of
of
and Information
University, Dhaka,
University, Dhaka,