Home automation using web application and speech recognition

Baby, Cyril Joe; Munshi, Nalin; Pant, Kamal K.; Dogra, Kunal; Rajesh, R.

doi:10.1109/icmdcs.2017.8211543

Cited by 12 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The client can specifically login and cooperate with the inserted gadget progressively without the need to keep up an extra server. Baby, Munshi, Malik, Dogra and Rajesh (2017) proposed an empowering mechanism for home automation with web application for electrical apparatuses (such as fan and light) control. They are dependent on sensor inputs to indicate movements and temperature.…”

Section: Related Workmentioning

confidence: 99%

Dual biometric encrypted authentication using Rasperry Pi processor

Narasimhan¹,

Arunachalam²

2020

3C Tecnología

View full text Add to dashboard Cite

Security is one of the main concerns in many sectors especially in banking. Many protection mechanisms such as passwords and number locks, PIN numbers have been used to identify the correct person. The biometric protection mechanism using fingerprints are also implemented. To ensure more security double biometric factors are implemented in this paper. Voice is a powerful factor to identify a speaker who is holding the account in banks. In addition to voice, usual face biometric features also considered for security in bank lockers. Both are transformed into encrypted format and stored to avoid database hacking. In this, Raspberry Pi board is used for implementation. To manipulate voice, devices like USB microphone and sound cards are used. For processing face image Raspi Cam is used. When

show abstract

Section: Related Workmentioning

confidence: 99%

Dual biometric encrypted authentication using Rasperry Pi processor

Narasimhan¹,

Arunachalam²

2020

3C Tecnología

View full text Add to dashboard Cite

show abstract

“…In our implementation, the user client is a Web application that every authenticated user can access in the form of a website. In [13], an implementation with a Web application is presented, but does not use external services for NLP, voice synthesis, voice recognition and data storage.…”

Section: Related Workmentioning

confidence: 99%

Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture

Alexakis

Panagiotakis

Fragkakis

et al. 2019

Designs

View full text Add to dashboard Cite

The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.

show abstract

“…Speech recognition, a system that converts speech signals into text, plays a very important role for spoken understanding/dialogue systems. Some of their implementations are as the man-machine interface for instance in autonomous vehicles [1], home automation [2] and smart-office [3]. The complexity of speech recognition tasks increase with the size of the vocabulary used in the systems, style of speaking (reading or spontaneous speech), or the presence of environmental distortions [4] such as noise or reverberations.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition

Pardede¹,

Adhi

Zilvan

et al. 2023

IJ-AI

View full text Add to dashboard Cite

There are great interests in developing speech recognition using deep learning technologies due to their capability to model the complexity of pronunciations, syntax, and language rules of speech data better than the traditional hidden Markov model (HMM) do. But, the availability of large amount of data is necessary for deep learning-based speech recognition to be effective. While this is not a problem for mainstream languages such as English or Chinese, this is not the case for non-mainstream languages such as Indonesian. To overcome this limitation, we present deep features based on convolutional neural networks (CNN) for Indonesian large vocabulary continuous speech recognition in this paper. The CNN is trained discriminatively which is different from usual deep learning implementations where the networks are trained generatively. Our evaluations show that the proposed method on Indonesian speech data achieves 7.26% and 9.01% error reduction rates over the state-of-the-art deep belief networks-deep neural networks (DBN-DNN) for large vocabulary continuous speech recognition (LVCSR), with Mel frequency cepstral coefficients (MFCC) and filterbank (FBANK) used as features, respectively. An error reduction rate of 6.13% is achieved compared to CNN-DNN with generative training.

show abstract

Home automation using web application and speech recognition

Cited by 12 publications

References 4 publications

Dual biometric encrypted authentication using Rasperry Pi processor

Dual biometric encrypted authentication using Rasperry Pi processor

Control of Smart Home Operations Using Natural Language Processing, Voice Recognition and IoT Technologies in a Multi-Tier Architecture

Deep convolutional neural networks-based features for Indonesian large vocabulary speech recognition

Contact Info

Product

Resources

About