Speaker Independent Continuous Speech to Text Converter for Mobile
Application
*R.Sandanalakshmi, P.Abinaya viji, M.Kiruthiga, M.Manjari, A.Sharina
Department of Electronics and communication |Engineering
Pondicherry Engineering College
Puducherry, India
*sandanalakshmi@pec.edu
than resorting to primitive interfaces such as keyboards and
pointing devices. The need for speaker independent
continuous speech to conversion system lies at the core of
many rapidly growing application areas. A speaker
independent system is intended for use by any speaker.
Continuous speech means naturally spoken sentences,
separated by minimum silence which is used for detecting
boundaries. Continuous speech recognition is difficult
when compared to Isolated words speech recognition. A
speech interface would support many valuable applications
like telephone directory assistance, spoken database
querying for novice users, “handsbusy” applications in
medicine or fieldwork, office dictation devices and for
controlling electronic devices. Especially, it is useful for
embedded systems like smart phones and PDAs having
insufficient space for typing or touching and helpful for
controlling navigation during car driving. Also, it can be
used to build advanced security systems and ATM
machines.
At present, there have been a number of
successful commercial voice interfaces. The most
prominent example is Siri, the voice-activated personal
assistant built in the latest iphone. Speech recognition
products are also available in Android, the Windows Phone
platform, and most other mobile systems with considerable
limitations. The recognition accuracy and performance of a
system would degrade dramatically with small
modifications of speech signal or speaking environment. As
a result more computation and memory capacity are needed
for Speech recognition.
Speech acquisition is the first and foremost stage
of speech recognition. It is a process of acquiring raw
speech signals from user. The acquired speech needs to be
preprocessed before extracting information. The extracted
information is used for recognition. The most important
step in preprocessing is speech detection in the acquired
signal. The effectiveness of speech recognizer is crucially
dependent on its performance. This is an algorithm to
detect silence parts of a speech signal and remove it as it
does not provide any information. Speech pause detection
algorithm [1] is used which detects speech pause minima
by adaptively tracking minima in a noisy signal power

Abstract:
An efficient speech to text converter for mobile
application is presented in this work. The prime motive
is to formulate a system which would give optimum
performance in terms of complexity, accuracy, delay
and memory requirements for mobile environment. The
speech to text converter consists of two stages namely
front-end analysis and patte rn recognition. The front
end analysis involves preprocessing and feature
extraction. The traditional voice activity detection
algorithms which track only energy cannot successfully
identify potential speech from input because the
unwanted part of the spee ch also has some energy and
appears to be speech. In the proposed system , VAD
that calculates energy of high frequency part separately
as zero crossing rate to differentiate noise from speech
is used. Mel Frequency Cepstral Coefficient (MFCC) is
used as feature extraction method and Generalized
Regression Neural Network is used as recognizer.
MFCC provides low word error rate and better feature
extraction. Neural Network improves the accuracy.
Thus a small database containing all possible syllable
pronunciation of the user is sufficient to give
recognition accuracy closer to 100%. Thus the proposed
technique entertains realization of real time speaker
independent applications like mobile phones, PDAs etc.
Key words: Speech to text converter,
extraction, Neural metwork.

1.

Feature

INTRODUCTION

Speech recognition (SR) is the translation of spoken words
into text. It is also known as "automatic speech
recognition", "ASR", "computer speech recognition",
"speech to text", or "STT". Speech is a natural mode of
communication for people and it conveys some
information. This information can be used for different
purposes like authentication, text conversion or machine
control depending upon application. People feel so
comfortable to interact with computers via speech, rather

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envelope. An ideal voice activity detector needs to be
independent from application area and noise condition. But
at the same time, the VAD algorithm [2] should be of low
complex to facilitate real time application. Therefore
simplicity and robustness against noise are two essential
characteristics of a practicable voice activity detector which
is incorporated in the proposed system. The traditional
voice activity detection algorithms which track only energy
cannot successfully identify potential speech from input.
VAD should be most sensitive as the unwanted part of the
speech also has some energy and appears to be speech. This
demands to use a VAD that also calculates energy of high
frequency part separately as ZCR to differentiate noise
from speech [3].
The preprocessed signal needs to be represented in a
compact manner for further process. Speech carries much
linguistic information which is required for recognition
task. This feature extraction technique is based on Mel
scale. MFCC [4] overcomes this limitation. Vocal tract
including tongue, teeth etc. determines the sound generated
by the human. The shape of the vocal tract determines what
sound comes out. If we are able to determine this shape,
accurate representation of the speech sample can be
produced. The shape of the vocal tract manifests itself in
the envelope of the short time power spectrum, and the job
of MFCCs is to accurately represent this envelope. MFCC
is proved to be more efficient by comparing MFCC with
other feature extraction techniques.[5]. In recent years
Neural Network is beginning to gain importance. This
paved a path for efficient deployment of recognizer. The
usability of neural network for speech recognizer proved to
be a suitable technology [6] and it outperforms traditional
recognizers like Hidden Markov Model (HMM), Dynamic
Time Warping (DTW), Vector Quantization (VQ) etc. The
promising results were obtained using Radial Basis Neural
Network as recognizer [7]. It also is found that RBF trains
and tests faster than Multilayer Perceptron (MLP) Neural
Networks. Moreover RBF gives more accurate result than
MLP [8]. In the proposed system GRNN [9] is used as
recognizer. A Generalized Regression Neural Network is
often used for function approximation. It is similar to
Radial Basis Neural Network but has a special linear layer
in addition to RBF network. It is found that the
performance of GRNN is superior to the other classifiers
namely Linear and MLP Neural Networks [10].

waveform discrete in both time (sample) and amplitude
(quantize). This A-to-D conversion is generally
accomplished by digital signal processing hardware on the
computer‟s sound card. The spoken voice frequency lies
between 300 to 3400 Hertz. So a sampling frequency of 8
KHz is chosen to satisfy Nyquist criterion. The speech
signal is recorded with a sampling rate of 8 KHz.. And the
data is saved with distinct name with „.wav‟ as extension.
2.2 VOICE ACTIVITY DETECTION
Voice activity detection is a technique to detect
the un-silenced part of the incoming speech signal. In
general, the acquired speech signal of 1 second duration
(8000 samples) starts and ends with silence which accounts
for nearly 6000 samples. VAD can be performed by two
algorithms. The first algorithm uses signal features based
on energy level and the second algorithm uses signal
features based on the rate of zero crossings.
The
combination of both gives good result that is used in the
proposed system. A basic VAD works on the principle of
extracting measured features from the incoming audio
signal, which is divided into frame size of 150 ms duration
and frame increment size of 40 ms duration. The extracted
signal features based on energy level and zero crossing rate
from the audio signal are then compared to a threshold and
then VAD decision is computed.
STEP 1: If the feature of the input frame exceed the
estimated threshold value, a VAD decision (VAD = 1) is
computed which declares that speech is present.
STEP 2: Otherwise, a VAD decision (VAD = 0) is
computed which declares the absence of speech in the input
frame.
2.3 PRE-EMPHASIS
In order to flatten speech spectrum, a pre-emphasis
filter is used before spectral analysis. Its aim is to
compensate the high-frequency part of the speech signal
that was suppressed during the human sound production
mechanism. Thus high frequency signals have less
amplitude. This gives rise to a negative spectral slope and
is compensated by appropriate pre-emphasis filter. The ztransform of the filter is given by [2]:

2. PREPROCESSING

-

2.1 SPEECH ACQUISITION
An acoustic speech signal exists as pressure
variations in the air. A microphone converts these pressure
variations into an electric current. To process the speech
signal digitally, it is necessary to make the analog

-

(1)

Where „a‟ is the filter coefficient and is chosen as 0.9375
The output signal after pre-emphasis is given by [2]:
-

2

-

(2)

Where y(n) - output signal
s(n) - input speech signal

MFCCs are commonly derived as follows:


On short time scales the audio signal doesn't
change much. The frame time chosen is 20-40ms
frames.



The power spectrum of each frame is passed
through the FFT block. FFT is used for
identifying the frequencies present in the frame.



The logarithmic compression is performed to
match features more closely to what humans
actually hear
The periodogram spectral estimate still contains
a lot of information not required for Automatic
Speech Recognition (ASR). For this reason we
take clumps of periodogram bins and sum them
up to get an idea of how much energy exists in
various frequency regions. This is performed by
our Mel filter bank. The Mel scale tells us exactly
how to space our filter banks and how wide to
make them.

2.4 FRAMING
Speech is a highly non-stationary signal. Hence
speech analysis must be carried out on short segments
across which the speech signal is assumed to be stationary.
For this the speech signal is divided into frames of small
duration typically 20 to 40ms with overlap of 10 to 15ms
for short-time spectral analysis.
2.5 WINDOWING
Windowing minimizes the discontinuities by
tapering the signals to quite small values (nearly zeros) at
the edges of a frame. Hamming window is also called the
raised cosine window. The Hamming window defined as
[3]
-



(3)

The output speech signal can be described as


(4)
Each frame s(n) is multiplied by hamming window w(n) of
25ms duration.
3.

The inverse fast Fourier transform of the Mel log
power gives the MFCC coefficients.

4. SPEECH RECOGNIZER USING NEURAL
NETWORK
The role of a recognizer is to assign the feature
vector provided by the feature extractor to a category. It
aims at measuring the similarity between an input speech
and a reference pattern or model which is obtained during
training. A neural network is a computational program that
is designed to model the way in which the brain performs a
particular task or function of interest; the network is usually
implemented using electronic components or simulated in
software on a digital computer.

FEATURE EXTRACTION

Feature extraction is the process of distinguishing features
from the input signal. It is about reducing the
dimensionality of the input-vector while still maintaining
the uniqueness of the signal. Good features possess the
attributes of being simple to extract, invariant against time
translations and overall amplitude variation, and capable of
discriminating different phonetic categories. Features are
extracted using MFCC technique [4].
3.1 MEL-FREQUENCY CEPSTRAL COEFFICIENT
ALGORITHM

4.1 PROPOSED NN RECOGNIZER
The goal of speech regression analysis is to
model a recognizer by providing a finite set of observations
of speech and its associated target values such that it gives
most probable output value for an unknown speech input.
The regression equation can be expressed as [9]
(5)

(6)
Fig. 1 MFCC block diagram

3

where wij is the target output corresponding to input
training vector xi and jth output.
The above equations (5) and (6) can be implemented as the
following neural network structure.

O1

OUTPUT
LAYER

HIDDEN
LAYER 2
(S UMMATI
ON LAYER)

HIDDEN
LAYER 2
(PATTERN
LAYER)

wij hi

INPUT

Fig. 2 Regression neural network architecture



The input layer has neurons which represent the
input patterns. It passes the input vector to each
of the neuron in the pattern layer.
The pattern layer has one neuron for each pattern.
During training all variations of input vectors and
its associated desired output or target (w ij) will be
provided to the network. The neuron in the
pattern layer compute Ci for the input provided to
it.

X1

X2

.

X3 .
Xn

. . .

.

.

.

The above neural network is known as General
Regression Neural Network (GRNN). It is one of the radial
basis networks. The hidden layer neurons of this network
are referred as radial basis neurons as they employ radial
basis kernel function. The proposed network does superior
function approximation and thus is well suited for speech
recognition.
5. DATABASE CREATION
In order to facilitate the training and testing of the
recognizer, speech database is required. A variety of
speech samples were obtained from different speakers to
form the speech database. Three kinds of databases were
created for

Digit Recognition

Word recognition

Transcription of title

(7)
(8)

where

is the spread (width) of the kernel function.
Spread denotes the distance an input vector must
be from a neuron's Ci in order to be correctly classified.
The value of spread chosen for the implemented network is
0.5.The summation layer has two units N and D where N
and D calculates

. . .

Fig. 3 GRNN recognizer

During testing the neuron will compute hi using Ci and
Xi as shown below[9]:

computes

hi

h1 (σ, C1) .., hi (σ, Ci).., hn (σ, Cn )

INPUT
LAYER



Op

5.1 Digit Recognition:
The digit database consists of ten utterances „zero‟
to „nine‟ collected from both male and female speakers.
The database is divided into training and testing. Training
set is used to train the neural network. Testing set is used to
test the performance of neural network. For speaker
dependent recognition, excellent recognition accuracy
closer to 100% is achieved. This recognizer can be very
well adapted for voice dialing.

.

The output unit divides N by D which gives the prediction
result. The above implementation is illustrated in the figure
shown below:

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6. RESULTS AND DISCUSSION
6.1 Syllable Level Word Recognition:
To perform syllable based implementation of word
recognition, a separate database was created with speech
samples from 2 female speakers. 36 syllables, namely, „be‟,
„com‟, „in‟, „out‟, „ply‟, „pose‟, „press‟, „side‟, „sup‟ , „in‟,
„vi‟, „ta‟, „tion‟, „ti‟, „ma‟, „no‟, „re‟, „po‟, „si‟, „lo‟, „de‟,
„di‟, „ca, „car‟, „na‟, „mo‟, „go‟, „to‟, „lo‟, „la‟, „ra‟, „ing‟,
„vo‟, „re‟, „va‟, „do‟ were selected. 4 samples were taken
for each word. The number of words in a language is
numerous. Training a network for each word requires huge
memory which is not realistic. To reduce memory
requirement, language structure can be exploited. The
number of syllables in a language is comparatively much
lesser than that of words. A word is formed by the
combination of one or more syllables and many words
could be built up from a single syllable. This idea has lead
to training of another GRNN recognizer for syllables
instead of complete words. The recognizer was designed
with 36 syllables from which 80 plus words could be
formed. The testing of the recognizer gave heartening
results.

Fig. 4 Comparison chart of Recognizer performance
using MFCC

6.2 Transcription:
Transcription is a process of converting
continuous speech into text. For real-time scenario, speech
signal for recognition would never be an isolated word but
rather a continuous stream of words. Syllable level
recognition with minimum pauses can also extended for
transcription purposes. To perform transcription of the title
“SPEAKER INDEPENDENT CONTINUOUS SPEECH
TO TEXT CONVERSION SYSTEM” a separate database
was created, consisting of each word. The samples were
collected from both male and female speakers.

Table 1
Comparison table of different Feature
Extraction Techniques

Table.2 Recognition Accuracy

*

Accuracy = Number of words correctly recognized
Total number of words

Fig.5 Comparison chart
Extraction Techniques

5

for

different

Feature



Comparison chart for elapsed time

Fig 6 Comparison chart
Extraction Techniques

for

different

7. SUMMARY AND CONCLUSION
In this work, a continuous speech to text conversion
system has been modeled using VAD, MFCC and GRNN
network.. The proposed model has several advantageous
characteristics such as fast learning, flexible network size
and robustness to speaker variability (ability to recognize
the same words pronounced in various manners). GRNN
promises to be a successful and powerful alternative to the
conventional speech recognizers.
The designed recognition system with all the above
salient features has been utilized for developing a digit
recognition system, a syllable level word recognition
system and a transcription system. Digit recognition can
find applications in voice dialing systems. And it needs to
be a speaker independent system. In syllable level word
recognition system, the recognizer was trained for
syllables. These syllables were separately recognized and
concatenated to output the complete word as text. In
Transcription system, the duration of an input speech signal
is made longer and the corresponding text output is
obtained. The recognizer was trained for individual words.
This system acts as a basis for real time speech recognition
products, where input will never be a single word. Good
recognition accuracy has been achieved in both cases.
These implementations illustrate the potential of optimal
configurations of key ASR components.
Theoretically, the accuracy increases with the
increase in training data. As a result, memory needed also
increases. It is found that carefully forming the database
helps a lot in reducing memory requirements and increases
recognition accuracy. In training phase, the words are
spoken clearly so that it avoids general variations and
confusions. In testing phase, the speech signal with
minimum pauses should be given as an input. This enables
the recognizer to discriminate the words effectively. For
future work ,the language modeling of HMM can also be
utilized in neural network implementation to build an
efficient hybrid HMM-NN recognizer including better
acoustic modeling accuracy, better context sensitivity, more
natural discrimination and a more economical use of
parameters.

Feature

The table consisting the correlation values
between two same words ( ba and ba ), between two similar
sounding words ( ba and pa), between two distinct
word(one and eight), elapsed time and correlation
comparison with noise is shown. The correlation analysis
has been performed on RASTAPLP, MFCC, BFCC, RPLP,
MFPLP, PLP features. From the performance chart given
above it is obvious that the MFCC technique gives well
distinct features than other techniques.

REFERENCES
[1] M.Marzinzik and B.Kollmeir, “Speech Pause Detection
For Noise Spectrum Estimation By Tracking Power
Envelope Dynamics”, IEEE Transactions On Speech And
Audio Processing, Barcelona, Vol.10, No.2, Feb.2002, pp.
109-117.

Fig. 7 Screenshot of Graphical User Interface

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[2] M. H. Moattar and M. M. Homayounpour, “A simple
but efficient real-time Voice Activity Detection algorithm”,
17th European Signal Processing Conference (EUSIPCO),
Aug. 2009, pp. 2549 – 2553.

Dr.R.Sandanalakshmi, is currently working as Assisstant
Professor Dept. of Electronics and communication
Engineering, Pondicherry Engineering College. She has
total 12 years of teaching experience. Her research field of
interest includes QoS improvement for next generation
wireless networks, Non-invasive studies on prognosis of
dengue using signal processing methods, speech to text
conversion for mobile applications .

[3] Namgook Cho and Eun-Kyoung Kim, “Enhanced Voice
Activity Detection Using Acoustic Event Detection And
Classification”,IEEE
Transactions
On
Consumer
Electronics, Vol. 57, No. 1, Feb.2011, pp. 196-202.

P.Abinaya viji, M.Kiruthiga, M.Manjari, A.Sharina
completed Under Graduate B.Tech in the department of
Electronics and Communication Engineering, Pondicherry
Engineering College in April 2013 and placed in Core
companies of specialization.

[4] Wei HAN, Cheong-Fat CHAN, Chiu-Sing CHOY and
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