scholarly journals An Critical Analysis of Speech Recognition of Tamil and Malay Language Through Artificial Neural Network

2021 ◽  
Author(s):  
Kingston Pal Thamburaj ◽  
Kartheges Ponniah ◽  
Ilangkumaran Sivanathan ◽  
Muniisvaran Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Recognition ◽  
Data Entry ◽  
Recognition System ◽  
Visual Speech ◽  
Document Preparation ◽  
Multiple Sensors ◽  
Visual Speech Recognition ◽  
Artificial Neural

Human and Computer interaction has been a part of our day-to-day life. Speech is one of the essential and comfortable ways of interacting through devices as well as a human being. The device, particularly smartphones have multiple sensors in camera and microphone, etc. speech recognition is the process of converting the acoustic signal to a smartphone as a set of words. The efficient performance of the speech recognition system highly enhances the interaction between humans and machines by making the latter more receptive to user needs. The recognized words can be applied for many applications such as Commands & Control, Data entry, and Document preparation. This research paper highlights speech recognition through ANN (Artificial Neural Network). Also, a hybrid model is proposed for audio-visual speech recognition of the Tamil and Malay language through SOM (Self-organizing map0 and MLP (Multilayer Perceptron). The Effectiveness of the different models of NN (Neural Network) utilized in speech recognition will be examined.

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).

Convolution Neural Network Based Visual Speech Recognition System for Syllable Identification

2020 ◽  
Vol 13 ◽  
Author(s):  
Hunny Pahuja ◽  
Priya Ranjan ◽  
Amit Ujlayan ◽  
Ayush Goyal
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Real Time ◽  
Acoustic Property ◽  
Recognition System ◽  
Convolution Neural Network ◽  
Visual Speech ◽  
Large Set ◽  
Speech Recognition System ◽  
Visual Speech Recognition

Introduction: This paper introduces novel and reliable approach for speech impaired people to assist them to communicate effectively in real time. A deep learning technique named as convolution neural network is used as its classifier. With the help of this algorithm, words are recognized from an input which is a visual speech, disregards with its audible or acoustic property. Methods: This network extracts the features from mouth stances and different images respectively. With the help of a source, non-audible mouth stances are taken as an input and then segregated as subsets to get desired output. The Complete Datum is then arranged to recognize the word as an affricate. Results: Convolution neural network is one of the most effective algorithms that extracts features, performs classification and provides the desired output from the input images for speech recognition system. Conclusion: Recognizing the syllables at real time from visual mouth stances input is the main objective of the proposed method. When tested, datum accuracy and quantity of training sets is giving satisfactory output. A small set of datum is taken as first step of learning. In future, large set of datum can be considered for analyzing the data. Discussion: On the basis of type of Datum, network proposed in this paper is tested to obtain its precision level. A network is maintained to identify the syllables but it fails when syllables are of same set. Requirement of Higher end graphics pro-cessing units is there to bring down the time consumption and increases the efficiency of network.

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