Development of a speaker recognition system using wavelets and artificial neural networks

Woo, Siew Chan; Lim, Chee Peng; Osman, Rozianawaty

doi:10.1109/isimp.2001.925421

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…With super capacity, service virtualization, good security [8,9], high reliability, etc. Cloud computing is being in different fields [10,11].…”

Section: Cloud Servementioning

confidence: 99%

Research on Speech Recognition System with Speaker Identification Based on the Cloud Server

Liu¹,

Yuan²

2014

AMR

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The speech recognition system is not real-time, a speak identification method based on the cloud server is proposed to solve this problem. Firstly, the MFCC frequency cepstrum coefficient and the first order differential coefficient are extracted from the speech feature vector sequence to form 32 dimensional. And then the 32 dimensional speech feature vector is sent to the cloud server, the training speaker model and identification are done in the cloud server. Finally, the identification result is sent to the client.

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“…With super capacity, service virtualization, good security [8,9], high reliability, etc. Cloud computing is being in different fields [10,11].…”

Section: Cloud Servementioning

confidence: 99%

Research on Speech Recognition System with Speaker Identification Based on the Cloud Server

Liu¹,

Yuan²

2014

AMR

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Imposing boundary and interface conditions in multi-resolution wavelet Galerkin method for numerical solution of Helmholtz problems

Yousefi

Jafari

Moghaddam

2014

Computer Methods in Applied Mechanics and Engineering

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Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Sunny

Peter

Jacob

2012

2012 International Conference on Advances in Computing and Communications

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Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations.

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Development of a speaker recognition system using wavelets and artificial neural networks

Cited by 10 publications

References 12 publications

Research on Speech Recognition System with Speaker Identification Based on the Cloud Server

Research on Speech Recognition System with Speaker Identification Based on the Cloud Server

Imposing boundary and interface conditions in multi-resolution wavelet Galerkin method for numerical solution of Helmholtz problems

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

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