# Lexico-acoustic Neural-based Models for Dialog Act Classification

arXiv: https://arxiv.org/abs/1803.00831 · HTML: https://mlsanity.com/paper/1803.00831/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 8 of 8)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding](https://mlsanity.com/paper/2211.05869/) (2022)
- [On the Evaluation of Speech Foundation Models for Spoken Language Understanding](https://mlsanity.com/paper/2406.10083/) (2024)
- [Oh, Jeez! or Uh-huh? A Listener-aware Backchannel Predictor on ASR Transcriptions](https://mlsanity.com/paper/2304.04478/) (2023)
- [What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition](https://mlsanity.com/paper/2107.02294/) (2021)
- [A neural prosody encoder for end-ro-end dialogue act classification](https://mlsanity.com/paper/2205.05590/) (2022)
- [Spoken Language Understanding on Unseen Tasks With In-Context Learning](https://mlsanity.com/paper/2505.07731/) (2025)
- [A Universality-Individuality Integration Model for Dialog Act Classification](https://mlsanity.com/paper/2204.06185/) (2022)

## Abstract (arXiv)

Recent works have proposed neural models for dialog act classification in spoken dialogs. However, they have not explored the role and the usefulness of acoustic information. We propose a neural model that processes both lexical and acoustic features for classification. Our results on two benchmark datasets reveal that acoustic features are helpful in improving the overall accuracy. Finally, a deeper analysis shows that acoustic features are valuable in three cases: when a dialog act has sufficient data, when lexical information is limited and when strong lexical cues are not present.