Maximum-a-Posteriori-Based Decoding for End-to-End Acoustic Models

N Kanda, X Lu, H Kawai - IEEE/ACM Transactions on Audio …, 2017 - ieeexplore.ieee.org
This paper presents a novel decoding framework for acoustic models (AMs) based on end-to-
end neural networks (eg, connectionist temporal classification). The end-to-end training of
AMs has recently demonstrated high accuracy and efficiency in automatic speech
recognition (ASR). When using the trained AM in decoding, although a language model
(LM) is implicitly involved in such an end-to-end AM, it is still essential to integrate an
external LM trained with a large text corpus to achieve the best results. While there is no …

[CITATION][C] Maximum-a-Posteriori-Based Decoding for End-to-End Acoustic Models............. N. Kanda, X. Lu, and H. Kawai 1023 Teager–Kaiser Energy Operators for …

H Khalilian, IV Bajic, RG Vaughan, A Franck - ieeexplore.ieee.org
Table of Contents Page 1 MAY 2017 VOLUME 25 NUMBER 5 ITASFA (ISSN 2329-9290)
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