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This paper presents the design of an ASIC for the task of multi-speaker phoneme recognition in continuous speech environments. The phoneme recogniser is based on DWTs for feature extraction and the One-against-one SVM method, along a priorities scheme, for classification. The ASIC design was fabricated on an AMS 0.35µ CMOS C35B4C3 chip. The final ASIC design resulted into a chip size equal to 43.35mm...
This paper presents the design of a digital hardware implementation based on Discrete Wavelet Transforms (DWTs) for the task of feature extraction in a multi-speaker phoneme recognition system. This is the first research where the design of a hardware-based DWT design is directed towards a speech recognition application. In the proposed architecture, the lifting-scheme approach employing the orthogonal...
A phoneme recognition system based on Discrete Wavelet Transforms (DWT) and Support Vector Machines (SVMs), is designed for multi-speaker continuous speech environments. Phonemes are divided into frames, and the DWTs are adopted, to obtain fixed dimensional feature vectors. For the multiclass SVM, the One-against-one method with the RBF kernel was implemented. To further improve the accuracies obtained,...
This paper presents the design of a digital hardware implementation based on Support Vector Machines (SVMs), for the task of multi-speaker phoneme recognition. The One-against-one multiclass SVM method, with the Radial Basis Function (RBF) kernel was considered. Furthermore, a priority scheme was also included in the architecture, in order to forecast the three most likely phonemes. The designed system...
Four multiclass Support Vector Machines (SVMs) methods were designed for the task of speaker independent phoneme recognition. These are the All-at-once, One-against-all, One-against-one, and the Directed Acyclic Graph SVM (DAGSVM). The Discrete Wavelet Transform (DWT) 8 frequency band power percentages are used for feature extraction. All tests were carried out on the TIMIT database. Comparable recognition...
A phoneme recognition system based on Discrete Wavelet Transforms (DWTs) and Support Vector Machines (SVMs), is designed for speaker-independent continuous speech environments. This research studies the pitch variation present in a speech signal, due to gender difference, and whether an increase is obtained if male and female speakers are considered separately. The results obtained show, that the...
A speaker independent phoneme recognition system, based on Support Vector Machines (SVMs) method was improved by adding a priority scheme to forecast the three most likely phonemes. The system helps improve the obtained recognitions rate. For the phoneme recognition system, four multiclass SVMs methods, the All-at-once, One-against-all, One-against-one, and the Directed Acyclic Graph SVM (DAGSVM),...
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