Wang, Y., Chen, X., Gales, M.J.F. et al. (2 more authors) (2018) Phonetic and graphemic systems for multi-genre broadcast transcription. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2018 - Signal Processing and Artificial Intelligence: Changing the World, 15-20 Apr 2018, Calgary, AB, Canada. IEEE ISBN 9781538646595
Abstract
State-of-the-art English automatic speech recognition systems typically use phonetic rather than graphemic lexicons. Graphemic systems are known to perform less well for English as the mapping from the written form to the spoken form is complicated. However, in recent years the representational power of deep-learning based acoustic models has improved, raising interest in graphemic acoustic models for English, due to the simplicity of generating the lexicon. In this paper, phonetic and graphemic models are compared for an English Multi-Genre Broadcast transcription task. A range of acoustic models based on lattice-free MMI training are constructed using phonetic and graphemic lexicons. For this task, it is found that having a long-span temporal history reduces the difference in performance between the two forms of models. In addition, system combination is examined, using parameter smoothing and hypothesis combination. As the combination approaches become more complicated the difference between the phonetic and graphemic systems further decreases. Finally, for all configurations examined the combination of phonetic and graphemic systems yields consistent gains.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Hidden Markov models; Phonetics; Acoustics; Training; Mathematical model; Context modeling; Task analysis |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Oct 2019 08:56 |
Last Modified: | 09 Oct 2019 12:33 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2018.8462353 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:150522 |