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        <title>The role of context in neural pitch accent detection in English</title>
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        <namePart type="given">Elizabeth</namePart>
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    <abstract>Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task. Our model makes greater use of context by using full utterances as input and adding an LSTM layer. We find that these innovations lead to an improvement from 87.5% to 88.7% accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result. We also find that a simple baseline that just predicts a pitch accent on every content word yields 82.2% accuracy, and we suggest that this is the appropriate baseline for this task. Finally, we conduct ablation tests that show pitch is the most important acoustic feature for this task and this corpus.</abstract>
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%0 Conference Proceedings
%T The role of context in neural pitch accent detection in English
%A Nielsen, Elizabeth
%A Steedman, Mark
%A Goldwater, Sharon
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F nielsen-etal-2020-role
%X Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task. Our model makes greater use of context by using full utterances as input and adding an LSTM layer. We find that these innovations lead to an improvement from 87.5% to 88.7% accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result. We also find that a simple baseline that just predicts a pitch accent on every content word yields 82.2% accuracy, and we suggest that this is the appropriate baseline for this task. Finally, we conduct ablation tests that show pitch is the most important acoustic feature for this task and this corpus.
%R 10.18653/v1/2020.emnlp-main.642
%U https://aclanthology.org/2020.emnlp-main.642
%U https://doi.org/10.18653/v1/2020.emnlp-main.642
%P 7994-8000

Markdown (Informal)

[The role of context in neural pitch accent detection in English](https://aclanthology.org/2020.emnlp-main.642) (Nielsen et al., EMNLP 2020)

• The role of context in neural pitch accent detection in English (Nielsen et al., EMNLP 2020)

ACL

• Elizabeth Nielsen, Mark Steedman, and Sharon Goldwater. 2020. The role of context in neural pitch accent detection in English. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7994–8000, Online. Association for Computational Linguistics.