This paper demonstrates the application of the Laplacian eigenmaps latent variable model (LELVM) to the task of speech recognition. LELVM is a new dimension ...
This paper demonstrates the application of the Laplacian eigenmaps latent variable model (LELVM) to the task of speech recognition.
Article "Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy" Detailed information of the J-GLOBAL ...
Author's Latest Publications. article. Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy · Author ...
Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy. A Jafari, F Almasganj. Speech Communication 52 ...
Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy. This paper demonstrates the application of the ...
Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy. Ayyoob Jafari, F. Almasganj. 2010, Speech ...
Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy. A Jafari, F Almasganj. Speech Communication 52 ...
The locality-preserving character of the Laplacian eigenmap algorithm makes it relatively insensitive to outliers and noise. It is also not prone to short ...
Missing: variable accuracy.
This feature extraction approach has showed very interesting improvement in speech recognition accuracy with about 6% improvement in isolated phoneme.