Time delay neural network: Difference between revisions

Revision as of 17:06, 8 May 2025

Revision as of 12:09, 10 May 2025

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=== State of the art ===

TDNN-based phoneme recognizers compared favourably in early comparisons with HMM-based phone models.<ref name="phoneme detection" /><ref name=":3" /> Modern deep TDNN architectures include many more hidden layers and sub-sample or pool connections over broader contexts at higher layers. They achieve up to 50% word error reduction over [[Mixture model|GMM]]-based acoustic models.<ref name=":4">Vijayaditya Peddinti, Daniel Povey, Sanjeev Khudanpur, ''[https://web.archive.org/web/20180306041537/https://pdfs.semanticscholar.org/ced2/11de5412580885279090f44968a428f1710b.pdf A time delay neural network architecture for efficient modeling of long temporal contexts]'', Proceedings of Interspeech 2015</ref><ref name=":5">David Snyder, Daniel Garcia-Romero, Daniel Povey, ''[http://danielpovey.com/files/2015_asru_tdnn_ubm.pdf A Time-Delay Deep Neural Network-Based Universal Background Models for Speaker Recognition]'', Proceedings of ASRU 2015.</ref> While the different layers of TDNNs are intended to learn features of increasing context width, they do model local contexts. When longer-distance relationships and pattern sequences have to be processed, learning states and state-sequences is important and TDNNs can be combined with other modelling techniques.<ref name=":6">{{Cite web |lastlast1=Haffner |firstfirst1=Patrick |last2=Waibel |first2=Alex |date=1991 |title=Multi-State Time Delay Networks for Continuous Speech Recognition |url=https://proceedings.neurips.cc/paper_files/paper/1991/hash/069d3bb002acd8d7dd095917f9efe4cb-Abstract.html |url-status=live |website=proceedings.neurips.cc |publisher=NIPS |pages=135-142135–142}}</ref><ref name=":1" /><ref name=":2" />

== Applications ==

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== References ==

{{reflist}}<ref>{{Cite journal |lastlast1=Haffner |firstfirst1=Patrick |last2=Waibel |date=1991 |orig-date=January 1991 |editor-last=Lippman |editor-first=Richard |editor2-last=Moody |editor2-first=John |title=Multi-State Time Delay Networks for Continuous Speech Recognition |url=https://www.researchgate.net/publication/221618146_Multi-State_Time_Delay_Networks_for_Continuous_Speech_Recognition221618146 |journal=Advances in Neural Information Processing Systems |publisher=Morgan Kaufman |volume=4 |pages=135-142135–142}}</ref>

<ref>{{Cite journal |lastlast1=Hampshire |firstfirst1=John |last2=Waibel |first2=Alex |orig-date=November 30, 1989 |editor-last=Touretzky |editor-first=David |title=Connectionist Architectures for Multi-Speaker Phoneme Recognition |url=http://papers.nips.cc/paper/213-connectionist-architectures-for-multi-speaker-phoneme-recognition |journal=Advances in Neural Information Processing Systems 2 |page=203-210}}</ref>

<ref>{{Cite journal |lastlast1=Waibel |firstfirst1=Alex |last2=Hanazawa |first2=Toshiyuki |last3=Hinton |first3=Geoffrey |last4=Shikano |first4=Kiyohiro |last5=Lang |first5=Kevin |date=April 1989 |title=Phoneme recognition using time-delay neural networks |url=https://www.researchgate.net/publication/391037926_Phoneme_Recognition_Using_Time-Delay_Neural_Networks#fullTextFileContent391037926 |journal=Acoustics, Speech and Signal Processing, IEEE Transactions on |volume=37 |issue=3 |pages=328 - 339328–339 |doi=10.1109/29.21701}}</ref>

<ref>{{Cite journal |last=Waibel |first=Alex |date=1987 |orig-date=December |title=Phoneme Recognition Using Time-Delay Neural Networks |url=https://www.researchgate.net/publication/391037926_Phoneme_Recognition_Using_Time-Delay_Neural_Networks#fullTextFileContent391037926 |journal=Conference: Meeting of the Institute of Electrical, Information and Communication Engineers (IEICE) |___location=Japan}}</ref>

[[Category:Neural network architectures]]