# Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions

## TL;DR

This paper introduces a combined CNN and CRF approach for dialog act classification on automatic transcriptions, demonstrating improved accuracy and analyzing the impact of different speech recognition systems.

## Contribution

It presents a novel CNN-CRF model for context-aware dialog act classification and evaluates its effectiveness across various automatic speech recognition outputs.

## Key findings

- CNN-CRF improves classification accuracy
- End-to-End ASR systems are more suitable for DA classification
- Consistent performance gains across datasets

## Abstract

This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional random fields (CRFs) for context modeling in DA classification. We explore the impact of transcriptions generated from different automatic speech recognition systems such as hybrid TDNN/HMM and End-to-End systems on the final performance. Experimental results on two benchmark datasets (MRDA and SwDA) show that the combination CNN and CRF improves consistently the accuracy. Furthermore, they show that although the word error rates are comparable, End-to-End ASR system seems to be more suitable for DA classification.