scholarly journals A Speaker Identity Recognition System based on Deep Learning

2019 ◽  
Vol 3 (5) ◽  
Author(s):  
Yili Shen
Keyword(s):  
Pattern Recognition ◽  
Signal Processing ◽  
Deep Learning ◽  
Speech Recognition ◽  
Digital Signal Processing ◽  
Digital Signal ◽  
Recognition System ◽  
Speech Recognition System ◽  
Identity Recognition ◽  
Voice Control

This paper describes a branch of pattern recognition and lies in the field of digital signal processing. It is a speech recognition system of identifying different people speaking based on deep learning. In brief, this method can be used as intelligent voice control like Siri.

scholarly journals IMPLEMENTATION OF SPEECH RECOGNITION SYSTEM USING DSP PROCESSOR ADSP2181

2012 ◽  
pp. 1-6
Author(s):  
KALPANA JOSHI ◽  
NILIMA KOLHARE ◽  
V.M. PANDHARIPANDE
Keyword(s):  
Signal Processing ◽  
Speech Recognition ◽  
Digital Signal Processing ◽  
Speech Processing ◽  
Speaker Recognition ◽  
Digital Signal ◽  
Recognition System ◽  
Theory And Practice ◽  
Clock Frequency ◽  
Application Development

While many Automatic Speech Recognition applications employ powerful computers to handle the complex recognition algorithms, there is a clear demand for effective solutions on embedded platforms. Digital Signal Processing (DSP) is one of the most commonly used hardware platform that provides good development flexibility and requires relatively short application development cycle.DSP techniques have been at the heart of progress in Speech Processing during the last 25years.Simultaneously speech processing has been an important catalyst for the development of DSP theory and practice. Today DSP methods are used in speech analysis, synthesis, coding, recognition, enhancement as well as voice modification, speaker recognition, language identification.Speech recognition is generally computationally-intensive task and includes many of digital signal processing algorithms. In real-time and real environment speech recognisers applications, it’s often necessary to use embedded resource-limited hardware. Less memory, clock frequency, space and cost related to common architecture PC (x86), must be balanced by more effective computation.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.

Advances in Deep Learning for Digital Signal Processing in Coherent Optical Modems

2020 ◽  
Author(s):  
Maxim Kuschnerov ◽  
Maximilian Schaedler ◽  
Christian Bluemm ◽  
Stefano Calabro
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Digital Signal Processing ◽  
Digital Signal

scholarly journals Keyword Recognition Device Cloud Based

2021 ◽  
Vol 9 (VIII) ◽  
pp. 87-89
Author(s):  
Apurv Singh Yadav
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Speech Recognition ◽  
Internet Of Things ◽  
Recognition System ◽  
The Internet ◽  
Speech Recognition System ◽  
The Past ◽  
Use Of The Internet ◽  
The Internet Of Things

Over the past few decades speech recognition has been researched and developed tremendously. However in the past few years use of the Internet of things has been significantly increased and with it the essence of efficient speech recognition is beneficial more than ever. With the significant improvement in Machine Learning and Deep learning, speech recognition has become more efficient and applicable. This paper focuses on developing an efficient Speech recognition system using Deep Learning.

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