Time delay neural network: Difference between revisions

Revision as of 19:49, 28 October 2018

Revision as of 08:41, 30 December 2018

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== History ==

The TDNN was first proposed to classify [[phonemes]] in speech signals for automatic [[speech recognition]], where the automatic determination of precise segments or feature boundaries is difficult or impossible. Because the TDNN recognizes phonemes and their underlying acoustic/phonetic features, independent of position in time, it improved performance over static classification.<ref name="phoneme detection" /><ref name=":0">Alexander Waibel, ''Phoneme Recognition Using Time-Delay Neural Networks'', SP87-100, Meeting of the Institute of Electrical, Information and Communication Engineers (IEICE), December, 1987,Tokyo, Japan.</ref> It was also applied to two-dimensional signals (time-frequency patterns in speech,<ref name=":1">John B. Hampshire and Alexander Waibel, ''Connectionist Architectures for Multi-Speaker Phoneme Recognition'', Advances in Neural Information Processing Systems, 1990, Morgan Kaufmann.</ref> and coordinate space pattern in OCR.<ref name=":2">Stefan Jaeger, Stefan Manke, Juergen Reichert, Alexander Waibel, ''Online handwriting recognition: the NPen++recognizer'', International Journal on Document Analysis and Recognition Vol. 3, Issue 3, March 2001</ref>).

==Overview==