ISSN:2229-6093 Y Bala Krishna et al,Int.J.Computer Techology & Applications,Vol 3 (1),163-168

ZIGBEE BASED VOICE CONTROL SYSTEM FOR SMART HOME Y. Bala Krishna1, S. Nagendram 2 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] 1

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.

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.

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

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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

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ISSN:2229-6093 Y Bala Krishna et al,Int.J.Computer Techology & Applications,Vol 3 (1),163-168

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

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].

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 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

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.

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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 speakerindependent speech recognition feature library inside.

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ISSN:2229-6093 Y Bala Krishna et al,Int.J.Computer Techology & Applications,Vol 3 (1),163-168

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].

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 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

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].

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

Figure 8: Voice recognition application hierarchy

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

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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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.

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 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

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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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spoken accent. These factors need to be studied further in more details by conducting more tests.  

“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. 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-ofsight 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

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REFERENCES [1] T. Birtley, (2010) Japan debates care for elderly. [Cited 21/09/2010]. Available: http://www.youtube.com/watch?v=C0UTqfigSec [2] Guangming Song, Fei Ding, Weijuan Zhang and Aiguo Song, “A Wireless Power Outlet System for Smart Homes,” IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, NOVEMBER 2008 [3](2010) uControl Home security system website. [Cited 201014thOct].Available: http://www.itechnews.net/2008/05/20/ucontrolhome-security-system/ [4] R. Gadalla, “Voice Recognition System for Massey University Smart house,” M. Eng thesis, Massey University, Auckland, New Zealand, 2006. [5] (2010) Home Automated Living website. [Cited 2010 14th Oct].Available: http://www.homeautomatedliving.com/default.htm [6] L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals, New Jersey, US: Prentice Hall Inc, 2001 [7] B. Yukesekkaya, A. A. Kayalar, M. B. Tosun, M. K. Ozcan, and A. Z. Alkar, “A GSM, Internet and Speech Controlled WirelessInteractive Home Automation System,” IEEE Transactions on Consumer Electronics, vol. 52, pp. 837-843, August 2006. [8] Jinn-Kwei Guo, Chun-Lin Lu, Ju-Yun Chang, Yi-Jing Li,Ya-Chi Huang, Fu-Jiun Lu and ChingWen Hsu, “Interactive Voice-Controller Applied to Home Automation,” 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing

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[9] Voice Recoder Reference Design (AN 278), Silicon Laboratories, 2006. [10] Guangming Song, Fei Ding, Weijuan Zhang and Aiguo Song, “A Wireless Power Outlet System for Smart Homes,” IEEE Transactions on Consumer Electronics, Vol. 54, No. 4, NOVEMBER 2008 [11] Il-Kyu Hwang Dae-Sung Lee Jin-Wook Baek “Home Network Configuring Scheme for All Electric Appliances Using ZigBee-based Integrated Remote Controller,” IEEE Transactions on Consumer Electronics, Vol.55, No.3, AUGUST2009

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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

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