13,949 research outputs found
Implementasi Speech Recognition Berbasis Android Dalam Optimalisasi Komunikasi Bagi Penyandang Tunarungu
This study aims to implement Android-based Speech Recognition or Speech to Text to make it easier to communicate with deaf people, so that everyone who wants to communicate does not need to understand certain sign languages when interacting with the deaf person. The research method in developing the system uses the SDLC (System Development Life Cycle) method or often referred to as the waterfall approach. The method used to identify voice using the Vector Quantization method, which is a method for conducting learning in a supervised competitive layer, with the Vector Quantization method it can be concluded that it has a better sound accuracy and clarity value with a performance result of 93%. from the results of a survey conducted from 45 people with hearing impairment, the application that was built played a very good role in terms of the continuity of the interaction process that was carried out very smoothly and easily understood by the deaf.
Keywords— Speech recognition, Speech to Text, Vector Quantizatio
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Speech is a natural, convenient and rapid means of human communication. The
abil ity to respond to spoken language is of special importance in computer
application wherein the user cannot use his/her limbs in a proper way, and may be
useful in office automation systems. It can help in developing control systems for
many applications such as in telephone assistance systems.
Rough sets theory represents a mathematical approach to vagueness and uncertainty.
Data analysis, data reduction, approxi mate classification, machine learning, and
discovery of pattern in data are functions performed by a rough sets analysis. It was
one of the first non-statistical methodologies of data analysis. It extends classical set
theory by incorporating into the set model the notion of classification as
indiscernibility relation.In previous work rough sets approach application to the field of speech recognition
was limited to the pattern matching stage. That is, to use training speech patterns to
generate classification rules that can be used later to classify input words patterns.
In this thesis rough sets approach was used in the preprocessing stages, namely in the
vector quantization operation in which feature vectors are quantized or classified to a
finite set of codebook classes. Classification rules were generated from training
feature vectors set, and a modified form of the standard voter classification
algorithm, that use the rough sets generated rules, was applied.
A vector quantization model that incorporate rough sets attribute reduction and rules
generation with a modified version of the K-means clustering algorithm was
developed, implemented and tested as a part of a speech recognition framework, in
which the Learning Vector Quantization (LVQ) neural network model was used in
the pattern matching stage.
In addition to the Arabic speech data that used in the original experiments, for both
speaker dependant and speaker independent tests, more verification experiments
were conducted using the TI20 speech data.
The rough sets vector quantization model proved its usefulness in the speech
recognition framework, however it can be extended to different applications that
involve large amounts of data such as speaker verification
Speech Recognition Using Vector Quantization through Modified K-meansLBG Algorithm
In the Vector Quantization, the main task is to generate a good codebook. The distortion measure between the original pattern and the reconstructed pattern should be minimum. In this paper, a proposed algorithm called Modified K-meansLBG algorithm used to obtain a good codebook. The system has shown good performance on limited vocabulary tasks. Keywords: K-means algorithm, LBG algorithm, Vector Quantization, Speech Recognitio
Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model
Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy
Word And Speaker Recognition System
In this report, a system which combines user dependent Word Recognition and text dependent speaker recognition is described. Word recognition is the process of converting an audio signal, captured by a microphone, to a word. Speaker Identification is the ability to recognize a person identity base on the specific word he/she uttered. A person's voice contains various parameters that convey information such as gender, emotion, health, attitude and identity. Speaker recognition identifies who is the speaker based on the unique voiceprint from the speech data. Voice Activity Detection (VAD), Spectral Subtraction (SS), Mel-Frequency Cepstrum Coefficient (MFCC), Vector Quantization (VQ), Dynamic Time Warping (DTW) and k-Nearest Neighbour (k-NN) are methods used in word recognition part of the project to implement using MATLAB software. For Speaker Recognition part, Vector Quantization (VQ) is used. The recognition rate for word and speaker recognition system that was successfully implemented is 84.44% for word recognition while for speaker recognition is 54.44%
Speaker Recognition Based Home Automation Using Matlab
Due to decline in both physical and mental abilities, some elderly are not allowed to leave the bed without assistance. Some time they are unable to make the desirable bodily movements and repositioning. In this paper the home automation is obtained using MATLAB based speaker recognition. The feature extraction of speech signal is done by using MFCC and for selection of features of speech signal vector quantization is used. By using above two steps the speaker is recognized and then this is given to the microcontroller by using serial communication .Then the particular home appliance get operated
A Review on Emotion Recognition Algorithms using Speech Analysis
In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed
VOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS
ABSTRACTObjective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hardproblem, requiring a combination of many techniques; however modern methods have been able to achieve an impressive degree of accuracy. Theobjective of this work is to examine various speech and speaker recognition techniques and to apply them to build a simple voice recognition system.Method: The project is implemented on software which uses different techniques such as Mel frequency Cepstrum Coefficient (MFCC), VectorQuantization (VQ) which are implemented using MATLAB.Results: MFCC is used to extract the characteristics from the input speech signal with respect to a particular word uttered by a particular speaker. VQcodebook is generated by clustering the training feature vectors of each speaker and then stored in the speaker database.Conclusion: Verification of the speaker is carried out using Euclidian Distance. For voice recognition we implement the MFCC approach using softwareplatform MatlabR2013b.Keywords: Mel-frequency cepstrum coefficient, Vector quantization, Voice recognition, Hidden Markov model, Euclidean distance
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