A comparative analysis of the topological structures of different LPC feature-based speech models
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
International Joint Conference on Neural Networks
First Page
2954
Last Page
2958
Publication Date
7-1999
Abstract
Describes initial experimentations done on three LPC (linear predictive coding) derived feature-based speech models: the LPC-cepstrum, the LSP (line spectral pair) and the postfilter-cepstrum (PFL). A comparative analysis of the topological structures of these models is also given. The structures are basically self-organizing feature maps which accept these models as inputs and after training, used to distinguish between isolated word utterances and speakers. A small database of 5 utterances and 4 speakers is initially used. The performance index of isolated word recognition and speaker identification for all models are calculated based on a hit-and-miss ratio and are also discussed. Experimental results reveal that the three parameters are comparable in performance. The LSP has a slight edge over the other two feature vectors in distinguishing isolated words.
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Digitial Object Identifier (DOI)
10.1109/IJCNN.1999.835989
Recommended Citation
Dadios, E. P., Palomar, L. R., & Fukuda, T. (1999). A comparative analysis of the topological structures of different LPC feature-based speech models. International Joint Conference on Neural Networks, 2954-2958. https://doi.org/10.1109/IJCNN.1999.835989
Disciplines
Engineering
Keywords
Self-organizing maps; Topology; Signal processing—Digital techniques
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
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