This paper presents a comparison of three competitive learning methods for vector quantizations of speech data in an efficient way.
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This study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1).
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Posted: Feb 19, 2023
Posted: Feb 19, 2023
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PDF | We present a decision-tree based procedure to quantize the feature-space of a speech recognizer, with the motivation of reducing the computation.
In this paper, we discuss the influence of feature vectors contributions at each learning time t on a sequential-type competitive learning algorithm.
Codebook design plays a crucial role in the performance of signal processing systems based on vector quantization (VQ). This paper is concerned with methods for ...
May 6, 2024 · This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for ...
The neural gas is a simple algorithm for finding optimal data representations based on feature vectors. The algorithm was coined "neural gas" because of the ...
We compare a number of training algorithms for competitive learning networks applied to the problem of vector quantization for data compression.
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This paper provides a comprehensive study of use of Artificial Neural. Networks (ANN) in speech recognition. The paper focuses on the different neural network ...