# Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech

arXiv: https://arxiv.org/abs/1706.00612 · HTML: https://mlsanity.com/paper/1706.00612/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2017.

## Built on by (most cited first, 18 of 18)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Signature Methods in Machine Learning](https://mlsanity.com/paper/2206.14674/) (2022)
- [EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2103.08310/) (2021)
- [Group Gated Fusion on Attention-based Bidirectional Alignment for Multimodal Emotion Recognition](https://mlsanity.com/paper/2201.06309/) (2022)
- [Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams](https://mlsanity.com/paper/2304.10740/) (2023)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [Speaker Attentive Speech Emotion Recognition](https://mlsanity.com/paper/2104.07288/) (2021)
- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning](https://mlsanity.com/paper/2202.02611/) (2022)
- [Dual-Encoder Architecture with Encoder Selection for Joint Close-Talk and Far-Talk Speech Recognition](https://mlsanity.com/paper/2109.08744/) (2021)
- [Use of Affective Visual Information for Summarization of Human-Centric Videos](https://mlsanity.com/paper/2107.03783/) (2021)
- [Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention](https://mlsanity.com/paper/2103.02362/) (2021)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers](https://mlsanity.com/paper/2306.13804/) (2023)
- [Investigations on Audiovisual Emotion Recognition in Noisy Conditions](https://mlsanity.com/paper/2103.01894/) (2021)
- [Optimizing Speech-Input Length for Speaker-Independent Depression Classification](https://mlsanity.com/paper/2501.00608/) (2024)
- [Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition](https://mlsanity.com/paper/2604.01711/) (2026)

## Abstract (arXiv)

Speech emotion recognition is an important and challenging task in the realm of human-computer interaction. Prior work proposed a variety of models and feature sets for training a system. In this work, we conduct extensive experiments using an attentive convolutional neural network with multi-view learning objective function. We compare system performance using different lengths of the input signal, different types of acoustic features and different types of emotion speech (improvised/scripted). Our experimental results on the Interactive Emotional Motion Capture (IEMOCAP) database reveal that the recognition performance strongly depends on the type of speech data independent of the choice of input features. Furthermore, we achieved state-of-the-art results on the improvised speech data of IEMOCAP.