Deep Learning for Prosody-Based Irony Classification in Spontaneous Speech

Helen Gent, Chase Adams, Chilin Shih, Yan Tang

Research output: Contribution to journalConference articlepeer-review

Abstract

Recognizing irony in speech and text can be challenging even for humans. For natural language processing (NLP) applications, irony recognition presents a unique challenge as irony alters the sentiment and meaning of the words themselves. Combining phonological insights from past literature on irony prosody and deep learning modeling, this research presents a new approach to irony classification in naturalistic speech data. A new corpus consisting of nearly five hours of irony-annotated, naturalistic, conversational speech data has been constructed for this study. A wide array of utterance-level and time-series acoustic features were extracted from this data and utilized in the training and fine-tuning of a series of deep learning approaches for irony classification. The best-performing model achieved an area under the curve of $0.811$ in the speaker dependent condition, and $0.738$ in the speaker independent condition, outperforming most irony classification models in the existing literature. In addition to the myriad real-world applications for this approach, its contribution to the understanding of prosodically-encoded augmentation of semantic content constitutes a significant step forward for research in the fields of linguistics and NLP.
Original languageEnglish (US)
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2022-September
StatePublished - 2022

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