scholarly journals Recognition Using DNN with Bacterial Foraging Optimization Using MFCC Coefficients

2021 ◽  
Vol 54 (2) ◽  
pp. 283-287
Author(s):  
Gottumukkala HimaBindu ◽  
Gondi Lakshmeeswari ◽  
Giddaluru Lalitha ◽  
Pedalanka P.S. Subhashini
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
User Authentication ◽  
Speech Comprehension ◽  
Mel Frequency Cepstral Coefficients ◽  
Bacterial Foraging Optimization ◽  
Speech Technology ◽  
Bacterial Foraging ◽  
Long Time ◽  
User Recognition

Speech is an important mode of communication for people. For a long time, researchers have been working hard to develop conversational machines which will communicate with speech technology. Voice recognition is a part of a science called signal processing. Speech recognition is becoming more successful for providing user authentication. The process of user recognition is becoming more popular now a days for providing security by authenticating the users. With the rising importance of automated information processing and telecommunications, the usefulness of recognizing an individual from the features of user voice is increasing. In this paper, the three stages of speech recognition processing are defined as pre-processing, feature extraction and decoding. Speech comprehension has been significantly enhanced by using foreign languages. Automatic Speech Recognition (ASR) aims to translate text to speech. Speaker recognition is the method of recognizing an individual through his/her voice signals. The new speaker initially privileges identity for speaker authentication, and then the stated model is used for identification. The identity argument is approved when the match is above a predefined threshold. The speech used for these tasks may be either text-dependent or text-independent. The article uses Bacterial Foraging Optimization Algorithm (BFO) for accurate speech recognition through Mel Frequency Cepstral Coefficients (MFCC) model using DNN. Speech recognition efficiency is compared to that of the conventional system.

scholarly journals Voice Controlled Vehicle Dashboard

2020 ◽  
Vol 9 (1) ◽  
pp. 1022-1027
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
Feature Matching ◽  
Heavy Traffic ◽  
Recognition Rate ◽  
Recognition System ◽  
Low Noise ◽  
Raspberry Pi ◽  
Mel Frequency Cepstral Coefficients ◽  
Power Spectral

Driving a vehicle or a car has become tedious job nowadays due to heavy traffic so focus on driving is utmost important. This makes a scope for automation in Automobiles in minimizing human intervention in controlling the dashboard functions such as Headlamps, Indicators, Power window, Wiper System, and to make it possible this is a small effort from this paper to make driving distraction free using Voice controlled dashboard. and system proposed in this paper works on speech commands from the user (Driver or Passenger). As Speech Recognition system acts Human machine Interface (HMI) in this system hence this system makes use of Speaker recognition and Speech recognition for recognizing the command and recognize whether the command is coming from authenticated user(Driver or Passenger). System performs Feature Extraction and extracts speech features such Mel Frequency Cepstral Coefficients(MFCC),Power Spectral Density(PSD),Pitch, Spectrogram. Then further for Feature matching system uses Vector Quantization Linde Buzo Gray(VQLBG) algorithm. This algorithm makes use of Euclidean distance for calculating the distance between test feature and codebook feature. Then based on speech command recognized controller (Raspberry Pi-3b) activates the device driver for motor, Solenoid valve depending on function. This system is mainly aimed to work in low noise environment as most speech recognition systems suffer when noise is introduced. When it comes to speech recognition acoustics of the room matters a lot as recognition rate differs depending on acoustics. when several testing and simulation trials were taken for testing, system has speech recognition rate of 76.13%. This system encourages Automation of vehicle dashboard and hence making driving Distraction Free.

scholarly journals Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks

2018 ◽  
Vol 2 ◽  
pp. 23-31 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Speaker Recognition ◽  
Linear Prediction ◽  
Rate Sensitivity ◽  
Second Phase ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Perceptual Linear Prediction

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well.

Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition

2020 ◽  
Vol 24 (3) ◽  
pp. 171-181
Author(s):  
A. Benmachiche ◽  
A. Makhlouf ◽  
T. Bouhadada
Keyword(s):  
Speech Recognition ◽  
Optimization Algorithm ◽  
Markov Models ◽  
Transition Probabilities ◽  
Hidden Markov ◽  
Iterative Algorithms ◽  
Gaussian Mixture ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.

THE VISUAL-AUDIO INTEGRATED RECOGNITION METHOD FOR USER AUTHENTICATION SYSTEM OF PARTNER ROBOTS

2011 ◽  
Vol 08 (04) ◽  
pp. 691-705 ◽  
Author(s):  
JIANGTAO CAO ◽  
NAOYUKI KUBOTA ◽  
PING LI ◽  
HONGHAI LIU
Keyword(s):  
Speech Recognition ◽  
Speaker Recognition ◽  
Visual Information ◽  
User Authentication ◽  
Recognition Algorithm ◽  
Final Decision ◽  
Acoustic Environment ◽  
Visual Background ◽  
Authentication System ◽  
Audio Information

Some of noncontact biometric ways have been used for user authentication system of partner robots, such as visual-based recognition methods and speech recognition. However, the methods of visual-based recognition are sensitive to the light noise and speech recognition systems are perturbed to the acoustic environment and sound noise. Inspiring from the human's capability of compensating visual information (looking) with audio information (hearing), a visual-audio integrating method is proposed to deal with the disturbance of light noise and to improve the recognition accuracy. Combining with the PCA-based and 2DPCA-based face recognition, a two-stage speaker recognition algorithm is used to extract useful personal identity information from speech signals. With the statistic properties of visual background noise, the visual-audio integrating method is performed to draw the final decision. The proposed method is evaluated on a public visual-audio dataset VidTIMIT and a partner robot authentication system. The results verified the visual-audio integrating method can obtain satisfied recognition results with strong robustness.

scholarly journals Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction

2021 ◽  
pp. 095440542110144
Author(s):  
B Birch ◽  
CA Griffiths ◽  
A Morgan
Keyword(s):  
Speech Recognition ◽  
Voice Recognition ◽  
Human Robot Interaction ◽  
Hole Drilling ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Robot Interaction ◽  
Extraction Algorithm ◽  
Dynamic Time ◽  
Manufacturing Environments

Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high.

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