Neural Network Architectures for Speaker Independent Phoneme Recognition

被引：0

作者：

Cutajar, M. ^{[1
]}

Gatt, E. ^{[1
]}

Grech, I ^{[1
]}

Casha, O. ^{[1
]}

Micallef, J. ^{[1
]}

机构：

[1] Univ Malta, Fac ICT, Dept Microelect & Nanoelect, MSD-2080 Msida, Malta

来源：

PROCEEDINGS OF THE 7TH INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS (ISPA 2011) | 2011年

关键词：

SPEECH RECOGNITION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Two different neural network architectures were designed for speaker independent phoneme recognition systems. The first architecture consists of the Radial Basis Function (RBF), while in the second architecture a Self-Organising Maps (SOM) neural network replaces the RBF. The Discrete Wavelet Transform (DWT) is used for feature extraction in both systems. Both systems were tested on the TIMIT database. The highest recognition rates obtained are 36.3% and 46.7%, for the RBF and SOM architectures respectively for multi-speaker unlimited vocabulary speech.

引用

页码：90 / 94

页数：5

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