ZIGBEE BASED VOICE CONTROL SYSTEM FOR SMART HOME
Y. Bala Krishna1, S. Nagendram 2 1 Research Scholar, Dept. of Electronics & Computer Engineering, K.L. University, A.P, India
2 Assistant Professor, Dept. of Electronics & Computer Engineering, K.L. University, A.P, India
[email protected], [email protected]
Abstract
This paper details the overall design of a wireless
home automation system (WHAS). This is fuelled by
the need to provide supporting systems for the elderly
and the disabled, especially those who live alone. The
automation centre’s on recognition of voice
commands and uses low-power RF ZigBee wireless
communication modules which are relatively cheap.
The home automation system is intended to control
all lights and electrical appliances in a home or
office using voice commands. The zigbee can receive
the voice and send the voice data to the ARM9
controller and then the controller converts the voice
into required format and then again send the data
through the zigbee to the another zigbee and to the
micro controller where the devices are attached to it.
Based on the message it received it either turns
ON/OFF the devices.
1. Introduction The demography of the world population shows a
trend that the elderly population worldwide is
increasing rapidly as a result of the increase of the
average live expectancy of people [1]. Caring for and
supporting this growing population is a concern for
governments and nations around the globe. Home
automation is one of the major growing industries
that can change the way people live. Some of these
home automation systems target those seeking luxury
and sophisticated home automation platforms; others
target those with special needs like the elderly and
the disabled. The aim of the reported Wireless Home
Automation System (WHAS) is to provide those with
special needs with a system that can respond to voice
commands and control the on/off status of electrical
devices, such as lamps, fans, television etc, in the
home. The system should be reasonably cheap, easy
to configure, and easy to run. There have been
several commercial and research projects on smart
homes and voice recognition systems. Many new
communication technologies such as GSM/GPRS
networks, wireless sensor networks, Bluetooth,
power line carriers and the Internet have been applied
to home automation. For example, wireless sensor
networks based on ZigBee protocol is widely used in
smart homes and it has become the focus in this field.
It consists of comfort and home automation, security
and safety at home, ambient assistance (intelligence)
and remote health monitoring. Guangming Song
(Etc) [2] developed a wireless-controllable power
outlet system.
Figure 1: uControl Home Security, Monitoring
and Automation (SMA) [3].
There have been several commercial and research
projects on smart homes and voice recognition
systems. Figure 1 shows an integrated platform for
home security, monitoring and automation (SMA)
from uControl [3]. The system is a 7-inch touch
screen that can wirelessly be connected to security
alarms and other home appliances. The home
automation through this system requires holding and
interacting with a large panel which constraints the
physical movements of the user [4]. Another popular
commercially available system for home automation
is from Home Automated Living (HAL) [5]. HAL
software taps the power of an existing PC to control
the home. It provides speech command interface. A
big advantage of this system is it can send commands
Y Bala Krishna et al,Int.J.Computer Techology & Applications,Vol 3 (1),163-168
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all over the house using the existing highway of
electrical wires inside the home’s walls. No new
wires means HAL is easy and inexpensive to install.
However, most of these products sold in the market
are heavily priced and often require significant home
make over.
2. System Overview The Wireless Home Automation System (WHAS) is
an integrated system to facilitate elderly and disabled
people with an easy-to-use home automation system
that can be fully operated based on speech
commands. The system is constructed in a way that is
easy to install, configure, run, and maintain.
Figure 2: Functional block diagram of the
Wireless Home Automation System (WHAS).
Legends- A: Analogue, D: Digital
Figure 2 illustrates the sequence of activities in the
WHAS. The voice is captured using a microphone,
sampled, filtered and converted to digital data using
an analogue-to-digital converter. The data is then
compressed and sent serially as packets of binary
data. At the receiving end (Central Controller
Module), binary data are converted to analogue,
filtered and passed to the computer through the sound
card. A Visual Basic application program, running on
the PC, uses Microsoft Speech API library for the
voice recognition. Upon recognition of the
commands, control characters are sent wirelessly to
the specified appliance address. Consequently,
appliances can be turned ON or OFF depending on
the control characters received. The voice is captured
using a microphone, sampled, filtered and converted
to digital data using an analogue-to-digital converter.
3. Hardware Design In this section we present the hardware descriptions
of the three modules that constitute the WHAS.
3.1 Handheld Microphone Module (MM) The components of the microphone module are shown
in Figure 3. The system captures human voice using a
sampling rate (fs) of 8 kHz. It is known that the
highest frequency component of the human voice is
20 kHz, however the most significant parts of the
information is encoded in frequencies between 6 Hz
and 3.5 kHz [6].
Figure 3: Block diagram of the handheld
Microphone Module.
To meet Nyquist sampling criteria, an anti-aliasing
filter is used to block all the frequencies above the
Nyquist frequency (Fn).
3.2 Central Controller Module The functional blocks of the central controller
module are shown in Figure 4. At the central
controller module (coordinator), when data are
received, the received bytes are decompressed using
DPCM algorithm [7]. Decompressed data is assigned
to the digital-to-analogue converter (DAC). The
analogue output of the DAC is filtered and fed to the
computer as analogue signal through the sound card
of the PC.
Figure 4: Block diagram of the Central Controller
Module.
3.3 Appliance Control Module
Once the speech commands are recognised, control
charterers are sent to the specified appliance address
through ZigBee communication protocol. Each
appliance that has to be controlled has a relay
controlling circuit. The speech recognition system
uses a single-chip solution for voice recognition.
LD3320 is a voice chip for speech recognition based
on SI-ASR (speaker-independent automatic speech
recognition) technology. LD3320 has a highly
effective speaker-independent speech recognition
search engine module and a complete speaker-
independent speech recognition feature library inside.
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It can complete speech recognition at an accuracy
rate of 95%, not even requiring users to do their
own voice training to generate speech features for
training library. So the cost of voice recognition
module is lower than SUNPLUS SPCE061A in [8].
Figure 5. Speech recognition process
The speech recognition process of LD3320 is
shown as Figure 5. It first analyses the spectrum of
the voice input by MIC and then extracts the voice
features. After that, it’s compared with words in the
list of key words. Finally, the key word with the
highest score is output as the recognition result.
4. Software Design
Software design includes ADC sampling and
compression/decompression algorithms,
transmission and receiving, and voice recognition.
4.1 ADC sampling and data compression /
decompression
The portable microphone module implements
DPCM compression scheme. This compression
algorithm is inherently lossy because of the error
incurred due to the nature of the compression
algorithm.
Figure 6: DPCM Compression algorithm
The algorithm compresses each ADC sample from
12 bits of data down to 6-bit codes. This code
represents the difference between the actual sample
and the predicted value of the sample. The
predicted sample is obtained from the previous
iteration result. The difference between the sample
and the predicted value is then quantised. The 6 bit
code is then packed into bytes of data in order to
send them serially. In order to calculate the new
predicted value, the compression algorithm decodes
the difference and adds it into the current predicted
value.
Figure 7: DPCM Decompression Algorithm
Figure 6 shows the DPCM compression algorithm.
At the receiving end, data are decompressed to the
original form using the DPCM decompression
algorithm. Figure 7 shows the decoding algorithm
which basically matches the received code with the
quantised difference and adds this difference to the
predictor [9].
4.2 Voice Recognition Application
The voice recognition application implements
Microsoft speech API. The application compares
incoming speech with an obtainable predefined
dictionary. The Microsoft speech API run time
environment relies on two main engines: Automatic
Speech Recognition (ASR engine) and Text To
Speech (TTS engine) as shown in Figure 8. ASR
implements the Fast Fourier Transform (FFT) to
compute the spectrum of the fingerprint data [4].
Figure 8: Voice recognition application
hierarchy
Comparing the fingerprint with an existing
database returns a string of the text being spoken.
This string is represented by a control character that
gets sent to the corresponding appliance’s address.
The designed graphical user interface (GUI) offers
the user the choice of selecting the desired serial
communication port as well as it provides a record
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ISSN:2229-6093
of all the commands that have been recognised and
executed. The application implements the hierarchy
described earlier in Figure 8 and the flow chart
shown in Figure 9. When designing the programme
GUI, making it a user friendly application was a
huge priority since the target clients need to avoid
any possible complications in the system. Control
characters corresponding to the recognised
commands are then sent serially from the central
controller module to the appliance control modules
that are connected to the home appliances.
Figure 9: Flow chart of the voice recognition
application
4.3 ZigBee RF communication
Zigbee protocol is the communication protocol
that’s used in this system. Zigbee offers 250 kbps
as maximum baud rate, however, 115200 bps was
used for sending and receiving as this was the
highest speed that the UART of the microcontroller
could be programmed to operate at. ZigBee is a
radio frequency (RF) communications standard
based on IEEE 802.15.4. Figure 2 depicts the
general architecture of a Zigbee based home
automation network [10]. All communication
between devices propagates through the
coordinator to the destination device. The wireless
nature of ZigBee helps overcome the intrusive
installation problem with the existing home
automation systems identified earlier. The ZigBee
standard theoretically provides 250kbps data rate,
and as 40kbps can meet the requirements of most
control systems, it is sufficient for controlling most
home automation devices [11]. The low installation
and running cost offered by ZigBee helps tackle the
expensive and complex architecture problems with
existing home automation systems, as identified
earlier.
5. Experimental Results and Discussions The prototype of the system has been fabricated
and tested. Figure 10 shows the microphone
module. Figure 11 shows the appliances control
module.
Figure 10: Microphone circuit board with
ZigBee module
Figure 11: Fabricated relay control unit
The graph in Figure 12 shows the response of the
speech recognition application to spoken
commands. The tests involved 35 subjects; the
trails were conducted with people with different
English accents. The test subjects were a mix of
male and female and 35 different voice commands
were sent by each person. Thus the test involved
sending a total of 1225 commands. 79.8% of these
commands were recognised correctly. When a
command is not recognised correctly, the software
ignores the command and does not transmit any
signals to the device control modules. The accuracy
of the recognition can be affected by background
noise, speed of the speaker, and the clearity of the
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ISSN:2229-6093
spoken accent. These factors need to be studied
further in more details by conducting more tests.
Figure 12: Results of voice recognition
experiments showing percentage of correct
recognition for different ethnicity/accent
The system was tested in an apartment and
performed well up to 40m. With a clear line-of-
sight transmission (such as in a wide open
gymnasium) the reception was accurate up to 80m.
Additional tests are being planned involving a
bigger variety of commands.
6. Conclusions and Future Work In this paper, we proposed a voice control system
for zigbee-based home automation networks. In
outwork, SI-ASR (Speaker-Independent Automatic
Speech Recognition) has been used; making it
requires no training of recording. This speech
recognition control system uses human-computer
interaction to realize multiple menus choose
function. A home automation system based on
voice recognition was built and implemented. The
system is targeted at elderly and disabled people.
The prototype developed can control electrical
devices in a home or office. The system
implements Automatic Speech Recognition engines
through Microsoft speech APIs. The system
implements the wireless network using ZigBee RF
modules for their efficiency and low power
consumption. Multimedia streaming through the
network was implemented with the help of the
Differential Pulse Code Modulation (DPCM)
compression algorithms that allows to compress the
speech data to half of its original data size. The
preliminary test results are promising.
Future work will entail:
Adding confirmation commands to the
voice recognition system.
Integrating variable control functions to
improve the system versatility such as
providing control commands other than
ON/OFF commands. For example
“Increase Temperature”, “Dim Lights”
etc.
Integration of GSM or mobile server to
operate from a distance.
Design and integration of an online home
control panel.
AKNOWLEDGEMENT
We thank to our principal, Prof. K. Raja Shekar
Rao, for providing necessary facilities towards
carrying out this work. We acknowledge the
diligent efforts of our Head of the Department
Dr.S.Balaji in assisting us towards
implementation of this idea.
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BIOGRAPHIES
Y. Bala Krishna, presently doing an M.Tech in Department of
Electronics and Computer
Engineering in Koneru
Lakshmaiah University.
S.Nagendram, presently working
in K.L.University as an Asst.
Professor in Electronics and Computer Engineering
Y Bala Krishna et al,Int.J.Computer Techology & Applications,Vol 3 (1),163-168
IJCTA | JAN-FEB 2012 Available [email protected]
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ISSN:2229-6093