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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">
== Applications ==
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