scholarly journals To Improve Voice Recognition System using GMM and HMM Classification Models

2019 ◽  
Vol 8 (11) ◽  
pp. 2724-2726
Keyword(s):  
Speech Recognition ◽  
Supervised Learning ◽  
Hybrid Model ◽  
Voice Recognition ◽  
Recognition System ◽  
Classification Model ◽  
Time Series Classification ◽  
Classification Models ◽  
Learning Patterns ◽  
The Individual

In this paper, the researcher study automatic speech recognition technology for the individual. We propose a new voice recognition system using a hybrid model GMM-HMM. HMM and GMM is a non-linear classification model. Each state in an HMM can be thought of as a GMM. HMM is consider observation for state. It is also known as time series classification model. In this model, samples have been trained independently and parameters consider jointly which provides better performance than other classification models. Speech recognition system consider two types of learning patterns such as supervised learning and unsupervised learning. In this context speaker dependent and speaker independent used for identifying the efficient and effective voice. In this paper researcher considered supervised learning model for recognize efficient voice. This new voice recognition system identifies incorrect phonemes and verifies the correctness of voice pronunciation. Using the GMM-HMM hybrid model produces better performance and effectiveness of voice

scholarly journals Automatic Speech Recognition in Mobile Customer Care Service

2015 ◽  
Vol 4 (01) ◽  
pp. 07-11
Author(s):  
Ms. Pooja Sahu
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Care Service ◽  
Recognition System ◽  
Care Services ◽  
Mobile Shopping ◽  
Customer Care ◽  
The One ◽  
The Individual ◽  
The Voice

In the project an automatic speech system is used in mobile customer care   services. In existing  mobile  customer care  services, customer  have  to  wait for 4 to 5 minutes  to get  into the  option  what   they  want to  inquire. Based on the requirement, we go for filtering the incoming calls. Persons who require particular data are dynamically move to speech recognition system that identifies the type of the enquiry chosen. Speech recognition is the one which dynamically identifies the individual speaking based on analyzing the speech waves. It helps in identifying the voice of the speaker to know the recognized user. It also helps in accessing services like telephone banking, mobile shopping, database services and securing the information which is confidential.

scholarly journals Review on Automated Elevator-an Attentive Elevator to Elevate using Speech Recognition

2021 ◽  
Vol 08 (1&2) ◽  
pp. 20-26
Author(s):  
Vishakha Patil ◽  
Keyword(s):  
Speech Recognition ◽  
Present Status ◽  
Voice Recognition ◽  
Recognition System ◽  
Modern World ◽  
Motor Module ◽  
The City ◽  
The Voice ◽  
Transport Device ◽  
Over Time

Elevator has over time become an important part of our day-to-day life. It is used as an everyday transport device useful to move goods as well as persons. In the modern world, the city and crowded areas require multiform buildings. According to wheelchair access laws, elevators/lifts are a must requirement in new multi-stored buildings. The main purpose of this project is to operate the elevator by voice command. The project is operating based on voice, which could help handicap people or dwarf people to travel from one place to another without the help of any other person. The use of a microcontroller is to control different devices and integrate each module, namely- voice module, motor module, and LCD. LCD is used to display the present status of the lift. The reading edge of our project is the “voice recognition system” which genet’s exceptional result while recognizing speech.

scholarly journals Voice Recognition System Through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4478-4483
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Voice Recognition ◽  
Recognition System ◽  
New Method ◽  
Human Voice ◽  
Developing Area ◽  
Voice Processing ◽  
The Voice

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

scholarly journals Speech recognition system for Turkish language with hybrid method

2017 ◽  
Vol 7 (1) ◽  
pp. 48-57
Author(s):  
Cigdem Bakir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Hybrid Model ◽  
Recognition System ◽  
Second Phase ◽  
Speech Recognition System ◽  
Feature Vectors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Currently, technological developments are accompanied by a number of associated problems. Security takes the first place amongst such problems. In particular, biometric systems such as authentication constitute a significant fraction of the security problem. This is because sound recordings having connection with various crimes are required to be analysed for forensic purposes. Authentication systems necessitate transmission, design and classification of biometric data in a secure manner. The aim of this study is to actualise an automatic voice and speech recognition system using wavelet transform, taking Turkish sound forms and properties into consideration. Approximately 3740 Turkish voice samples of words and clauses of differing lengths were collected from 25 males and 25 females. The features of these voice samples were obtained using Mel-frequency cepstral coefficients (MFCCs), Mel-frequency discrete wavelet coefficients (MFDWCs) and linear prediction cepstral coefficient (LPCC). Feature vectors of the voice samples obtained were trained with k-means, artificial neural network (ANN) and hybrid model. The hybrid model was formed by combining with k-means clustering and ANN. In the first phase of this model, k-means performed subsets obtained with voice feature vectors. In the second phase, a set of training and tests were formed from these sub-clusters. Thus, for being trained more suitable data by clustering increased the accuracy. In the test phase, the owner of a given voice sample was identified by taking the trained voice samples into consideration. The results and performance of the algorithms used for classification are also demonstrated in a comparative manner. Keywords: Speech recognition, hybrid model, k-means, artificial neural network (ANN).

Design and Implementation of Speech Recognition System Based on FPGA

2013 ◽  
Vol 416-417 ◽  
pp. 1156-1159
Author(s):  
Bo Nian Yi
Keyword(s):  
Speech Recognition ◽  
Information Technologies ◽  
Voice Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Characteristic Parameters ◽  
The Core ◽  
New Information ◽  
And Control ◽  
The Voice

Speech recognition technology is one of the hottest and the most promising new information technologies in the world. This paper studied the voice pretreatment and extractions of MFCC characteristic parameters, constructed speech keywords recognition algorithm with the core of the VQ model and the HMM model, using MATLAB to complete the training and simulation of algorithm, FPGA-based voice recognition technology, and the simulation and implementation of its hardware and software. It laid the foundation for the realization of speech recognition and control based FPGA.

scholarly journals Development of End – to – End Encoder - Decoder Model Applying Voice Recognition System in Different Channels

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2350-2352
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Error Rate ◽  
Voice Recognition ◽  
Ground Truth ◽  
Recognition System ◽  
Training Algorithms ◽  
Word Error Rate ◽  
End To End ◽  
Evaluation Metric

the dissimilarity in recognizing the word sequence and their ground truth in different channels can be absorbed by implementing Automatic Speech Recognition which is the standard evaluation metric and is encountered with the phenomena of Word Error Rate for various measures. In the model of 1ch, the track is trained without any preprocessing and study on multichannel end-to-end Automatic Speech Recognition envisaged that the function can be integrated into (Deep Neural network) – based system and lead to multiple experimental results. More so, when the Word Error Rate (WER) is not directly differentiable, it is pertinent to adopt Encoder – Decoder gradient objective function which has been clear in CHiME-4 system. In this study, we examine that the sequence level evaluation metric is a fair choice for optimizing Encoder – Decoder model for which many training algorithms is designed to reduce sequence level error. The study incorporates the scoring of multiple hypotheses in decoding stage for improving the decoding result to optimum. By this, the mismatch between the objectives is resulted in a feasible form to the maxim. Hence, the study finds the result of voice recognition which is most effective for adaptation.

Design and Implementation of Speech Recognition System Based on SPCE061A

2011 ◽  
Vol 187 ◽  
pp. 389-393
Author(s):  
Shu Rong Wang ◽  
Shao Huang ◽  
Fei Yuan
Keyword(s):  
Control System ◽  
Speech Recognition ◽  
Light Emitting Diode ◽  
Voice Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Light Emitting ◽  
Design And Implementation ◽  
Short Summary ◽  
Voice Control

Based on Sunplus SCM SPCE061A, this paper designs a voice control system which uses speech recognition technology. The results of light emitting diode reaction experiment review that the system can identify more than four orders. Finally, the paper makes a short summary and outlook of voice recognition technology.

scholarly journals Voice Recognition System for Door Access Control Using Mobile Phone

2021 ◽  
Vol 10 (9) ◽  
pp. 132-139
Author(s):  
Falohun Adeleye Samuel ◽  
Adedeji Oluyinka Titilayo ◽  
Adegbola Oluwole Abiodun ◽  
Alade Oluwaseun Modupe ◽  
Makinde Bukola Oyeladun ◽  
...  
Keyword(s):  
Control System ◽  
Speech Recognition ◽  
Access Control ◽  
Mobile Phone ◽  
Control Systems ◽  
Voice Recognition ◽  
Recognition System ◽  
Access Control System ◽  
Or Organization ◽  
People With Physical Disabilities

Security is one of the most important issues for any individual or organization, and as technology has advanced, numerous techniques to protecting lives and property have been deployed through door access control systems. The typical method of unlocking a door is to open it with a real key or by twisting the door knob. Physical keys that are used to open doors are subject to duplication and can be misplaced. Furthermore, typical biometric technologies and other technologies are vulnerable to a variety of failures, such as a person's finger being cut off to produce a fingerprint scan, a pin being hacked using various methods or permutations, and a person's photo being used for facial recognition. Furthermore, it is more difficult for people with physical disabilities to unlock a door system without the assistance or support of others. For example, it is difficult for a person in a wheelchair to open a door system without the assistance or support of another person. As a result, a speech recognition access control system that can accommodate both able-bodied and impaired people is unavoidable. This paper demonstrates how voice recognition may be used to access door systems by creating a door access control system that employs speech recognition to simplify the work of providing access to door systems via a mobile phone connected via Bluetooth. The system's performance is perfectly in line with its design.

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