implementation of a multi-lingual control of home appliance

37

Implementation of A Multi-Lingual Control of Home Appliance Features for Intuitive Feedback

ADEBISI John Adetunji Department of Electrical Electronics and Computer Engineering

University of Lagos, Akoka, Nigeria.

GBOLADE Olatunde Oluwatosin Department of Electrical Electronics and Computer Engineering

University of Lagos, Akoka, Nigeria.

BABAJIDE Samuel Afolabi Department of Computer Science and Engineering

Obafemi Awolowo University, Ile-Ife. Nigeria Abstract. There have been recent advances in the field of multilingual speech recognition and these breakthroughs have seen significant adoption in voice-controlled home automation systems. Spontaneous response to instructions from home appliances is a major challenge of voice recognition systems. This requirement involves a range of complex hardware and software design processes. The existing systems do not consider specific geographical regions especially the western parts of the world. As such, there is a very little support for local languages in Nigeria. This work presents a multilingual voice recognition system for controlling home appliances; allowing intuitive response from specific devices to for Nigerian people. It provides support for the major languages in the country including English, Yoruba, and Igbo and allows the control of multiple appliances. The system is achieved by using the Elechouse Voice Recognition Module V3 in conjunction with an Arduino Uno Microcontroller which in turn controls the state of the connected appliances. Speech collected through the microphone is passed into the voice recognition module and it compares the voice sample against previously stored commands. A corresponding control signal is sent by the microcontroller on successful recognition of the voice command. The project was evaluated to measure the accuracy of the constructed system in successfully controlling an appliance natively once a registered voice command is uttered. The results showed an accuracy of about 86% in quiet environments and 52% in noisy environments based on the system response in terms of speed and accuracy of response to voice commands in the selected languages. Key words: Multilingual, Voice recognition, Home Appliance, Microcontroller

Adebisi A., Gbolade O., Babajide A. (2021) Implementation of A Multi-Lingual Control of Home Appliance Features for Intuitive Feedback . In: Odumuyiwa V., Oladejo B., Onifade O., David, A. (eds) Transition from Observation to Knowledge to Intelligence (TOKI 2021) - Human - Data Interaction in an Artificial World, pp 37-54, Lagos, ISKO-WA, 2021.


38

1. Background Study

Home automation has witnessed increased development and

adoption in recent times. This development assist people especially the

working class, nursing mothers and all those involved in multitasking

to be more efficient with their time while controlling appliances

seamless using their voices. Many tools have been developed to help

automate basic tasks around the home and offices, hence giving people

more time to focus on productive work as outlined in Kraljevic et al,

(2018). To assist physically challenged, elderly men and women and

mothers who may be caught up with multiple home tasks, home

automation systems have developed rapidly to allow easy control of

lights, heating and cooling systems, alarm systems, security cameras,

sprinkler systems and several other appliances within the home. Most

home automation systems provide remote monitoring and access. The

connected appliances can be viewed and monitored through internet

connected devices like smartphones, tablets to mention few. While a

large proportion of the population is computer literate and have no

problem using these remote devices, there is still need to cater for those

who might not be as discussed in Ali et al, (2017).

AlShu'eili et al, (2011) stated breakthroughs in artificial intelligence

have greatly helped to achieve this task, specifically in the fields of

natural language processing. It is now possible for machines to

understand and process human languages, translate and even

summarize it. The development of voice assistants such as Siri, Alexa

and Cortana only help to underscore these advances. Voice recognition

and control has now become one of the most important features in home

automation systems. Considering the global trend, a lot of the world’s

population speak multiple languages, however many voice recognition

products still cater to a singular language. Hence this research designed,

constructed and evaluate a prototype for a multilingual speech control

interface, for multiple appliances.

1.1. Significance

This work improves upon existing voice-controlled home

automation systems by allowing users to interact with it in multiple

J.A. Adebisi, O. O. Gbolade & S.A. Babajide

______________________________________________________________ 39

languages. This is especially useful in areas where the language of

preference is not English or they speak comfortably in more than one

language such as Nigeria. It provides SMART control of multiple

appliances connected to the automation system in an intuitive manner

rather than the control of just a single appliance as it is in many

automation systems. The home automation system detects user’s

speech and whenever a recognized phrase is identified it triggers

corresponding action to switch appliances on or off. With speech

recognition, people seeking comfort and physically challenged people

can control their appliances with much more ease.

2. Theoretical Framework

Researchers over the years have studied several approaches to

achieve accurate speech recognition. In this section, several of these

models will be explored while detailing each’s drawbacks and

improvements over the other models. The Dynamic Time Warping

algorithm was widely used until the discovery of Hidden Markov

Models which has been largely utilized in recent times. Breakthroughs

in artificial intelligence specifically deep learning have also allowed for

the use of neural networks to develop even more sophisticated speech

recognition models. The speech recognition modules used in the

various cases such as the Google Speech API, Microsoft Azure,

Amazon AWS or custom designed modules responsible for the speech-

to-text conversion and make a comparative review of the several

systems.

2.1. Hidden Markov Models

The most relevant and widely used model in speech recognition

today is the Hidden Markov models (HMMs). It is a statistically-based

approach that outputs a sequence of symbols. A speech signal can be

seen as a piecewise signal or even a short-time stationary process. This

makes HMMs well suited for speech analysis. This is detailed in Niesler

and Willett (2008). HMMs can be automatically trained, are

computationally feasible and easy to use making them very popular. For


40

speech recognition, the Hidden Markov model outputs a sequence of

real-valued vectors which are n-dimensional, once every 10

milliseconds. The HMM will have for each of its state a statistical

distribution giving a likelihood/probability to each observed vector.

Each word/phoneme will have its own unique output distribution; the

final HMM for the sequence of words is obtained by summing together

the individual hidden trained models for all the words. This is the basis

of the most common method of speech recognition using HMMs. State-

of-the-art speech recognition systems utilize several combinations of

these standard techniques to gain improved results over the basic

approach outlined above and is shown in Caesar (2012).

HMM acoustic models tend to underestimate the acoustic

probability hence little weight is given to the language model. To solve

this problem the language model is scaled by an empirically determined

constant s. This is called the Language Model Scale Factor (LMSF).

The weighting has a side effect on inserting words. Hence, a scaling

factor p, was added to penalize word insertions. This is called the word

insertion penalty (WIP) which is also empirically obtained.

Then the likelihood of a word occurring becomes: Ẃ = 𝑎𝑟𝑔 max

𝑤𝑃(𝑊|𝑶)𝑃(𝑊)𝑠|𝑊|𝑝 (2.1)

In the log domain, the total likelihood is obtained as 𝑙𝑜𝑔|𝑤| Ẃ = 𝑙𝑜𝑔 |𝑤 |𝑃(𝑶|𝑊) + 𝑠 𝑙𝑜𝑔 |𝑤|𝑃(𝑊) + 𝑝 (2.2)

|W| represents the length of the word sequence W, s is the LMSF

(usually in the range 5 to 15) and p is the WIP (usually in the range 0 to

20). A separate notation can be used depending on whether the local or

global posterior probabilities are being considered. The global posterior

probability is given as 𝑃(𝑀|𝑶, 𝜃) where M is the model assumed model

O and parameters 𝜃 . Equation 2.3 Shows the global posterior

probability in terms of the local posterior probabilities: 𝑃 (𝑀|𝑶) = ∑ ∑ 𝑃(𝑞𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 , 𝑀|𝑶)𝐿

𝑙𝑛=1𝐿𝑙1=1 (2.3)

The posterior probability and the modal can be further decomposed

into the product of the language models and an acoustic model:


______________________________________________________________ 41

𝑃(𝑞𝑙11 , . . . , 𝑞𝑙𝑛

𝑛 , 𝑀|𝑶) = 𝑃(𝑞 𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 |𝑶)𝑃(𝑀|𝑶, 𝑞𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 ) (2.4)

≅ 𝑃(𝑞 𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 |𝑂)𝑃(𝑀, 𝑞𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 ) (2.5)

Where the acoustic model is 𝑃(𝑞𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 |𝑂) and 𝑃(𝑀, 𝑞𝑙1

1 , . . . , 𝑞𝑙𝑛𝑛 )

is the language model. Equation (2.4) can be modified to obtain:

𝑃(𝑀|𝑂) ∑ [ ∏ 𝑃( 𝑞 𝑙𝑛𝑛 | 𝑂 𝑛−𝑐

𝑛+𝑑 ) 𝑃( 𝑞 𝑙𝑛

𝑛 |𝑀)

𝑃( 𝑞 𝑙𝑛𝑛 )

𝑁𝑛 ] 𝑃(𝑀)𝑙1…...,𝑙𝑛 (2.6)

The hybrid HMM according to Lin et al, (2013) is based on the local posterior probability, and 𝑂 𝑛−𝑐

𝑛+𝑑 is limited to the local context only.

Bayes rule, showed that:

𝑃( 𝑂 𝑛−𝑐𝑛+𝑑 | 𝑞 𝑙𝑛

𝑛 )

𝑃( 𝑂 𝑛−𝑐𝑛+𝑑 )

= 𝑃( 𝑞 𝑙𝑛

𝑛 | 𝑂 𝑛−𝑐𝑛+𝑑 )

𝑃( 𝑞 𝑙𝑛𝑛 )

(2.7)

Equation 2.8 can be obtained as:

𝑃(𝑀|𝑂) = ∑ [ ∏ 𝑃( 𝑂 𝑛−𝑐𝑛+𝑑 | 𝑞 𝑙𝑛

𝑛 ) 𝑃( 𝑞 𝑙𝑛

𝑛 |𝑀)

𝑃( 𝑂 𝑛−𝑐𝑛+𝑑 )

𝑁𝑛 ] 𝑃(𝑀)𝑙1…...,𝑙𝑛 (2.8)

This is based on local likelihood; this is called the standard HMM as in Caesar (2012).

2.2. Dynamic Time Warping Algorithm

Niesler and Willett (2008) describe an alternate but less reliable

model for speech recognition is the Dynamic time warping (DTW)-

based approach. It was prevalently used for voice recognition but has

been widely replaced by the more efficient HMM-focused approach.

Dynamic time warping algorithm measures the similarity between two

distinct sequences varying in time or speed. In general, it helps to

determine the optimal matching between two sequences. This sequence

alignment approach is often employed in the context of HMMs.


42

2.3. Neural Networks

The emergence of neural networks as an effective acoustic

modelling approach came about in the late 1980s. They have then seen

significant adoption in many areas of speech/voice recognition

including phoneme classification, audiovisual speech recognition,

speaker adaptation and even isolated word recognition. According to

Lopez-Moreno et al, (2014) when neural networks are used for the

estimation of speech feature segments, they allow for discriminative

training in a very efficient way. However, despite their relative

effectiveness in classifying isolated words and individual phonemes

i.e., short-time units, early neural networks were seldom successful in

continuous recognition tasks. This was due to their limited ability to

effectively model time dependencies.

2.4. Empirical Review

In this section, methods employed in implementing multilingual

speech recognition systems starting from earlier developed approaches

were progressively explore and move on to current state-of-the-art

methods. Project where voice recognition has been integrated to

achieve control of the connected appliances were considered.

Adebisi et.al., (2021) worked on a multi-channel solution which

provides a suitable backup to the cloud is a viable solution to serve as a

failsafe plan in case of hardware eventualities. This work focuses on the

design and construction of a real-time power and data backup

surveillance system for security of lives and properties. Construction of

a sufficient power bank backup system was developed in real time with

average power outage duration in Nigeria considered. It shared similar

methodology with this work but more of security solution than home

automation.

Kumar et al, (2018) proposed a cost-effective and performance

efficient virtual assistant for home automation. The project used ideas

from Artificial Intelligence, Speech Recognition and Internet of Things.

The system receives voice commands from the user and responds


______________________________________________________________ 43

through voice by itself. It can retrieve dates, weather conditions, time,

return search results and play specific music tracks from the internet.

Node-MCU are responsible for controlling the appliances after

receiving commands from Raspberry Pi microcontroller. The

Raspberry Pi processes the voice input received through the

microphone, converts it into text and executes it. A python script puts

the whole system together as it contains both the speech-to-text and

text-to-speech code. Finally, the Node-MCU is coded with Arduino to

control the appliances.

Othman (2017) proposed using a voice controlled personal assistant

using the Alexa Voice Service (AVS) speech recognition engine

provided by Amazon. The author also used a Raspberry Pi as the

microcontroller responsible for command processing similar to the

method proposed in (Adebisi et al 2021). AVS is used as both the

speech-to-text engine for converting the audio into text form and also

as the Text-to-Speech engine for vocally outputting the retrieved

information. The system achieved a speech recognition accuracy of

92% and was very easy to implement due to the flexibility of the AVS

API.

In Matarneh et al, (2017) several existing speech recognition

systems were explored and compared. It focused on two main classes

namely open source and closed-source systems. The closed-source

refers to proprietary software with no access to the source code. Only

the binary versions are distributed. Under this class the Google Speech

Recognition API is present, Microsoft Speech API, Siri, Yandex speech

kit and Dragon Mobile SDK. The comparisons were based on accuracy,

speed, performance, response time, API and compatibility. The analysis

of these various systems allows us to make informed decisions when

choosing an appropriate one. The tests found CMU Sphinx to be the

most attractive open-source voice recognition system however it

requires a large initial database. Of the closed systems, the most

accurate was found to be Dragon but due to its complex API, Google

speech API was more attractive. In general, the closed systems were


44

found to be more accurate than the open-source voice recognition

systems.

Caesar (2012) proposed a multilingual speech recognition technique

that entails first detecting the language present in the audio signal. Then,

the outcome of the language identification determines which of the

available monolingual speech recognizers will be activated and passed

the voice input. The paper also proposed a method for the language

identification. It involved estimating the language posterior

probabilities giving the input signal using hierarchical multi-layer

perceptron in combination with hidden Markov models. The case study

was the Media-Parl speech corpus which consisted of recorded speech

in both Swiss-French and Swiss-German. The experiments showed that

using language identification to determine the appropriate speech

recognizer to be used significantly improves upon using a single

universal speech recognizer capable of recognizing multiple language

such as in Niesler and Willett (2008).

Niesler and Willett (2008) worked on a multilingual speech

recognition system to identify four different South African languages.

It showed specifically how to identify English, Zulu, Afrikaans and

Xhosa using a single recognition pass with a set of HMMs. The paper

shows the effect of using discriminatively trained acoustic models on

the language identification process and the in-language recognition

accuracy. To achieve this, they optimized the acoustic model

parameters of the speech recognizer by maximizing the value of the

training data posterior probability through a process called maximum

likelihood (ML) parameter estimation. This was achieved

computationally using the Baum-Welch algorithm at a low cost. It

suggests other discriminative measures which may produce better

parameter estimates resulting in better recognition accuracy.

Discriminative training proved to be computationally expensive and

showed no marked improvement in recognition accuracy overall the

baseline acoustic models. Finally code-mixing (using words from

multiple languages) seemed to have lower recognition accuracy rates. The studies have shown that systems that incorporate Deep Neural

Networks in combination with Hidden Markov models achieve the


______________________________________________________________ 45

highest speech recognition accuracy. A comparative review of many

commercial speech recognition systems was also conducted. The

Google Speech Recognition Application Programing Interface (API)

seemed to be the most favored due to its high accuracy rate and easy to

use interface.

2.5. Formulation of Speech Recognition

First is the division of the continuous speech waveform into frames

of constant length (~25ms). Each frame is then represented as a discrete

parameter vector. It is assumed that for the duration of a single vector

(i.e. frame) the waveform is stationary. This approximation helps to

easily formulate the problem. For each word in the speech signal, 𝑶 = 𝑜1𝑜2 · · · 𝑜𝜏 is a sequence of parameter vectors where 𝐨𝐭 is taken at a

time t ∈ {1, . . . , τ} . Ẃ = 𝑎𝑟𝑔 max

𝑤𝑃(𝑊|𝑶) (3.1)

Where Ẃ refers to the recognized word, then P means the probability

measure and 𝑊 = 𝑤1 … 𝑤𝑘 represents a word sequence. Bayes’ Rule

allows us to transform (3.1) into a calculable form:

𝑃(𝑊|𝑶) = (𝑃(𝑶|𝑊). 𝑃(𝑊))/𝑃(𝑶) (3.2)

Where 𝑃(𝑶|𝑊) signifies the acoustic model and 𝑃(𝑊) is the

language model. It can be inferred then that determining the most

probable word in the speech sample depends only on the likelihood

𝑃 (𝑶|𝑊) as shown in Niesler and Willett (2008).

3. Proposed System Architecture

The architecture of the proposed multilingual voice-controlled

automation system is depicted in Figure 3.1. It shows the flow of

operation from the collection of speech through the microphone to the

control of the connected appliances.


46

Figure1: Block Diagram of the Voice Controlled Automation System

The Arduino is powered by a 5V DC supply. The voice commands

are spoken by the user and are recorded by a microphone. The speech

input is then passed along to the voice recognition module where the

speech signal is matched against the pre-recorded trained voice

samples. After the successful recognition of a voice command, the

microcontroller actuates the corresponding connected appliance such as

setting off the alarm, turning ON/OFF the light and fan. (a)

3.1. Implementation

A simple collar type microphone was used for this project to capture

audio by converting observed sound waves into electrical energy

variations. The obtained signal was then amplified as an analogue signal

converted into a digital signal for further processing by a computer or

an audio device. Elechouse voice recognition module V3 was used for

the speech recognition process in the proposed system. It allows any

sound to be trained as a command. It is a two way controlled mechanism

with relatively moderate voltage requirement with easy speaking

recognition module compare to other voice recognition boards. In

addition. Elechouse supports up to 80 voice commands and up to seven

can work at the same time with trained commands. Table 4.1 shows the

breakdown of identified languages and selected appliances used for

testing during the implementation. This gives us the flexibility to train

the system to recognize commands spoken in English, Yoruba, Igbo and

Hausa and any other language of choice. The V3 module also has

recognition accuracy of 99% when used under ideal conditions (i.e. in


______________________________________________________________ 47

low noise levels). It can store up to 80 commands in its library and

recognize a total of 7 commands at the same time.

3.3.1 Arduino Microcontroller

The microcontroller used for the proposed system is the Arduino-

Uno microcontroller shown in Figure 1. It comes with an Integrated

Development Environment (IDE) which runs on a PC and allows users

write programs in C or C++ programming languages. This is where

most of the logic behind this project is implemented. The Arduino

microcontroller, powered by 5V DC, 16 MHz clock speed; is based on

the Atmega328 and has 14 digital and output pins with 6 analogue

inputs.

Figure 1: Arduino-Uno microcontroller

(Source: www.store.arduino.cc/arduino-uno-rev3)

A 5V 4-channel relay module, an electrical device that acts as a

switch allowing current passage act as the connecting interfacing

between the microcontroller and the devices. In addition to Arduino

Microcontroller, other components were identified. The voice

recognition module was trained with a diverse set of voices and

language samples. The voice recognition module chosen for this project

is the Elechouse V3 recognition module. Samples from three (3)

speakers were taken (one for each of the identified languages) to utter

voice commands so as to properly train the voice module. These voice

commands specify the operation to perform on the mentioned

appliance. The software implementation of the project consisted of

training the voice recognition module and writing a program to control

the connected appliances on recognition of a voice command. The voice


48

commands were recognized in each of the languages alongside the

appliance to be controlled. Table 1 shows the breakdown of the

applicable voice commands in the respective languages.

Table 1: All Identified Voice Commands

APPLIANCE ENGLISH YORUBA IGBO

LED/BULB “Light on” “tan ina” “gbanye ọkụ”

“Light off” “pa ina” “gbanyụọ ọkụ”

FAN “Fan On” “tan fan” “gbanye fan”

“Fan Off” “pa fan” “gbanyụọ fan”

BUZZER “Buzzer On” “tan buzzer” “gbanye

buzzer”

“Buzzer Off” “pa buzzer” “gbanyụọ

buzzer”

On successful matching of both commands, a prompt stating

“success” show. A snapshot of this process is shown in Figure 2. The

voice command has now been stored and ready for use. To verify that

the voice command has indeed been stored at the specified address, the

“record” command is typed, followed by the address. If the record is

already trained, it shows “record --> trained”.

To enable intuitive control of the appliances, the components are

first connected to the Arduino and the Arduino is programmed to

control them after successful recognition of a voice command. The

program written in C programming language was stored in a file called

“Vr_Control_Appliances”. The program code snippet as shown in

Figure 3.


______________________________________________________________ 49

Figure 2: Voice Recognition Module Training

Figure 3: Microcontroller program code

4. Results Discussion

To determine the performance of the system, the accuracy of the

system in recognizing voice commands and then controlling the

corresponding appliance is a major focus. In carrying out this

effectively, evaluation partitions were created. These partitions include

testing in a quiet environment, testing in a noisy environment and

testing using random/unregistered voice samples. In all aforementioned

partitions, the tests are carried out for each of the languages. For each


50

voice command, a total of 6 experimental trials as shown in Tables 2

and 3 are conducted to effectively measure the accuracy of the system

in responding to the command i.e. controlling the corresponding

appliance. A ‘1’ signifies success while a ‘0’ indicates failure. Several

voice commands (made up of turn/off commands in three different

languages for each of the connected appliance).

Table 2: Yoruba Voice Commands’ Test in a Quiet Environment

S/

N

Voice

Command

Experimental trials Total

Responses

1 2 3 4 5

1. tana 1 1 0 1 1 4

2. pana 1 1 1 1 0 4

3. tan-fan 0 0 1 1 1 3

4. pa-fan 1 0 1 1 0 3

5. tan-buzz 0 1 1 0 1 3

6. pa-buzz 1 1 1 1 1 5

Table 3: Igbo Voice Commands’ Test in a Quiet Environment

S/N Voice

Command

Experimental trials Total

Responses

1 2 3 4 5

1. gbanyeku 1 0 1 0 1 3

2. gbanyoku 0 1 1 1 1 4

3. gbanye-fan 0 1 1 0 1 3

4. gbanyo-fan 1 0 1 1 0 3

5. gbanye-

buzz

1 1 1 1 1 5

6. gbanyo-

buzz

1 1 0 1 1 4

From the tests conducted in a quiet environment, the results showed

that the English voice commands were more easily recognized when

compared to that of Yoruba and Igbo. The tests on English voice

commands attained a system response rate of about 80% while that of


______________________________________________________________ 51

Yoruba was about 73.3% response and Igbo 70% as depicted in Figure

4.

Figure 4: Response Rate of System in Quiet Environment

In a noisy environment, the accuracy of the system dropped significantly. The tests on English voice commands attained a system response rate of about 56.6% while that of Yoruba was about 43.3% response and Igbo 40%. The response rates are compared against one other and shown in Figure 5.

Figure 5: Response Rate of System in Noisy Environment

Tests was also conducted to ensure that it doesn’t respond to

untrained voice commands. The goal is to ensure that the system does

not respond to stray voice samples that have not been trained to control

an appliance. The results show that the system performs well in these


52

instances. Furthermore, results show that the system performs

extremely well in these instances by not acting on any of these stray

commands irrespective of how similar they might be to registered ones.

5. CONCLUSION

This work designed and constructed an intuitive multilingual voice

recognition for the control of home appliances. Most previous work in

this area are limited to a single language (English) while this project has

been able to effectively implement a multilingual voice recognition

system. The project was evaluated to measure the accuracy of the

constructed system in successfully controlling an appliance natively

once a registered voice command is uttered. The results showed an

accuracy of about 86% in quiet environments and 52% in noisy

environments based on the system response in terms of speed and

accuracy of response to voice commands in the selected languages.

Although the result of this work can be applied in other western part of

the world as it is configurable; the proposed solution has sufficiently

addressed the challenge of using local language to control basic home

appliances when the need arises. In future, effort will be put in place to

implement control of appliances in even more local Nigerian languages

and beyond to assist the elderly ones, handicaps among other categories

of citizen as the case may be.

List of References

Adebisi, J. A., Abdulsalam, K. A., & Adams, I. O. (2021). Multi-Power

Source and Cloud-Backup Enabled Security Framework for

Surveillance in Nigeria. In IOP Conference Series: Earth and

Environmental Science (Vol. 730, No. 1, p. 012005). IOP

Publishing.

Ali, A. T., Eltayeb, E., and Abusail, E. A. (2017). Voice Recognition

Based Smart Home Control System. International Journal of

Engineering Inventions.


______________________________________________________________ 53

AlShu'eili, H., Gupta, G. S., and Mukhopadhyay. (2011). Voice

Recognition Based Wireless Home Automation System. Kuala

Lumpur, Malaysia: International Conference on Mechanotrics.

Caesar, H. (2012). Integrating language identification to improve

multilingual speech recognition (No. REP_WORK). Idiap.

Gonzalez-Dominguez, J., Eustis, D., Lopez-Moreno, I., Senior, A.,

Beaufays, F., and Moreno, P. (2014). A Real-Time End-to-End

Multilingual Speech. IEEE.

Kraljevic, L., Russo, M., and Stella, M. (2018). Voice Command

Module for Smart Home Automation. International Journal of

Signal Processing.

Kumar, K., Jayalakshmi, J., and Prasanna, K. (2018). A Python based

Virtual Assistant using Raspberry Pi for Home Automation.

International Journal of Electronics and Communication

Engineering.

Lopez-Moreno, I., Gonzalez-Dominguez, J., Plchot, O., Martinez, D.,

Gonzalez-Rodriguez, J., and Moreno, P. (2014). Automatic

Language Identification Using Deep Neural Networks. IEEE

International Conference on Acoustic, Speech and Signal Processing

(ICASSP).

Matarneh, R., Maksymova, S., and Lyashenko, V. V. (2017). Speech

Recognition Systems: A Comparative Review. IOSR Journal of

Computer Engineering.

Niesler, T., and Willett, D. (2008). Language Identification and

Multilingual Speech Recognition using Discriminatively Trained

Acoustic Models. South Africa: Department of Electrical and

Electronics Engineering, University of Stellenbosch.

Othman, E. (2017). Voice Controlled Personal Assistant Using

Raspberry Pi. International Journal of Scientific and Engineering

Research.

Sabah, F., Jyoti, S., Miskin, P., and Sarvagya, M. (2018). Design and

Implementation of Voice Controlled Home Automation Using Iot.

International Conferene on Recent Trends in Engineering Science

and Management.


54

implementation of a multi-lingual control of home appliance

Documents