scholarly journals Voice Recognition System Using Natural Language Processing

2018 ◽  
Vol 6 (4) ◽  
pp. 4075-4077
Author(s):  
Swapnil Mohite
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Voice Recognition ◽  
Recognition System

scholarly journals Virtual Voice Assistant

2021 ◽  
Vol 9 (VI) ◽  
pp. 4097-4101
Author(s):  
Ch. V. Tejaswi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Voice Recognition ◽  
Computer Software ◽  
Human Beings ◽  
Desktop Application ◽  
Personal Assistant ◽  
The Voice

This is desktop application which can assist people with basic tasks using natural language. Virtual Voice Assistants can go online and search for an answer to a user’s question. Actions can be triggered using text or voice. Voice is the key. A virtual voice assistant is a personal assistant which uses natural language processing (NLP) , voice recognition and speech synthesis to provide a service through a particular application. Natural Language Processing in short is called as NLP. It is basically a branch of artificial intelligence which mainly deals with the interaction between personal computers and human beings using the natural language. The main objective of NLP is to read, convert, understand, and make use of the human languages in a manner that is valuable. Voice recognition is a hardware device or computer software program with the potential to decode the voice of human beings. Voice recognition is usually used to operate a gadget, execute commands, or write without making use of any mouse, keyboard, or press any buttons. Artificial production of human speech is called as Speech Synthesis. A system used for this purpose is called a speech computer or speech synthesizer and can be implemented in many products of software’s and hardware’s.

scholarly journals Aplikasi Personal Assistant Berbasis Voice Command Pada Sistem Operasi Android Dengan NLP

2020 ◽  
Vol 5 (2) ◽  
pp. 121
Author(s):  
Yoshua Constantin ◽  
Ucuk Darusalam ◽  
Novi Dian Nathasia
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Voice Recognition ◽  
Voice Command ◽  
Personal Assistant

Saat ini penggunaan smartphone sangat masif di seluruh dunia, khusus nya android. Fitur yang dimiliki sangat mudah dipakai, namun sebagian orang tidak mampu untuk menggunakan secara maksimal fitur-fitur yang ada karena membutuhkan waktu untuk berinteraksi dengan fitur tersebut, salah satu teknologi yang dapat dimanfaatkan untuk mengoptimalkan android, yaitu voice recognition namun teknologi yang sudah dikembangkan oleh vendor membutuhkan koneksi internet untuk dapat berjalan, untuk sebagian user yang tidak terdapat jaringan internet tidak bisa menikmati teknologi tersebut, untuk dapat mengatasi masalah ini dibutuhkan aplikasi personal assistant yang dapat digunakan ketika tidak ada koneksi internet sekalipun, ide yang didapat dari penciptaan aplikasi ini adalah untuk menyempurnakan kekurangan dari penelitian sebelumnya, dengan ada nya teknik Natural Language Processing (NLP) mempermudah dalam proses pembuatan aplikasi ini, aplikasi sudah diuji dengan serangkaian tes dan hasil dari tes tersebut menunjukkan bahwa instalasi dapat dilakukan di versi terbaru yaitu android 10 dan 10 dari 12 perintah yang diuji berhasil dilakukan dengan rata-rata respon selama 0.054 detik. Semua fitur dapat dijalankan tanpa perlu koneksi internet.

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

Designs ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 32 ◽  
Author(s):  
George Alexakis ◽  
Spyros Panagiotakis ◽  
Alexander Fragkakis ◽  
Evangelos Markakis ◽  
Kostas Vassilakis
Keyword(s):  
Natural Language Processing ◽  
Internet Of Things ◽  
Natural Language ◽  
Language Processing ◽  
Smart Home ◽  
Rapid Development ◽  
Voice Recognition ◽  
Global Network ◽  
The Internet ◽  
The Internet Of Things

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.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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