Method and apparatus for automatically updating estimates of undesirable components of the speech signal in a speech recognition system

1993 ◽  
Vol 93 (2) ◽  
pp. 1219-1219
Author(s):  
Vladimir Sejnoha
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Recognition System ◽  
Speech Recognition System

Voice-Based Speaker Identification and Verification

2021 ◽  
pp. 288-316
Author(s):  
Keshav Sinha ◽  
Rasha Subhi Hameed ◽  
Partha Paul ◽  
Karan Pratap Singh
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Signal ◽  
Reference Model ◽  
Speaker Identification ◽  
Recognition System ◽  
Speech Recognition System ◽  
Primary Focus ◽  
Dynamic Time ◽  
Dynamic Time Wrapping

In recent years, the advancement in voice-based authentication leads in the field of numerous forensic voice authentication technology. For verification, the speech reference model is collected from various open-source clusters. In this chapter, the primary focus is on automatic speech recognition (ASR) technique which stores and retrieves the data and processes them in a scalable manner. There are the various conventional techniques for speech recognition such as BWT, SVD, and MFCC, but for automatic speech recognition, the efficiency of these conventional recognition techniques degrade. So, to overcome this problem, the authors propose a speech recognition system using E-SVD, D3-MFCC, and dynamic time wrapping (DTW). The speech signal captures its important qualities while discarding the unimportant and distracting features using D3-MFCC.

scholarly journals Comparative Analysis of Methods Used to Extract Speech Signal Features

2021 ◽  
Vol 10 (10) ◽  
pp. 52-63
Author(s):  
Ziad A. Alqadi ◽  
Sayel Shareef Rimawi
Keyword(s):  
Comparative Analysis ◽  
Speech Recognition ◽  
Speech Signal ◽  
Recognition System ◽  
Sampling Frequency ◽  
Speech Recognition System ◽  
Important Process ◽  
Positive Effects ◽  
Signal Features ◽  
Speech Features

The stage of extracting the features of the speech file is one of the most important stages of building a system for identifying a person through the use of his voice. Accordingly, the choice of the method of extracting speech features is an important process because of its subsequent negative or positive effects on the speech recognition system. In this paper research we will analyze the most popular methods of speech signal features extraction: LPC, Kmeans clustering, WPT decomposition and MLBP methods. These methods will be implemented and tested using various speech files. The amplitude and sampling frequency will be changed to see the affects of changing on the extracted features. Depending on the results of analysis some recommendations will be given.

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.

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