Transfer Learning for Improving Speech Emotion Classification Accuracy

Published in

Interspeech(2018)

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TL;DR

Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and Support Vector Machine (SVM) baseline system.

Abstract

The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from the same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that are novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and Support Vector Machine (SVM) baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.

Authors

Junaid Qadir

5 papers

S. Latif

2 papers

R. Rana

2 papers

References36 items

Progressive Neural Networks for Transfer Learning in Emotion Recognition

Speech emotion recognition cross language families: Mandarin vs. western languages

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Cross-acoustic transfer learning for sound event classification

Emotion Classification from Noisy Speech - A Deep Learning Approach

Transfer Learning for Improving Speech Emotion Classification Accuracy

Published in

Interspeech(2018)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Junaid Qadir

5 papers

S. Latif

2 papers

R. Rana

2 papers

References36 items

Progressive Neural Networks for Transfer Learning in Emotion Recognition

Speech emotion recognition cross language families: Mandarin vs. western languages

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Cross-acoustic transfer learning for sound event classification

Emotion Classification from Noisy Speech - A Deep Learning Approach

Shahzad Younis

1 papers

TL;DR

Abstract

Authors

References36 items

Progressive Neural Networks for Transfer Learning in Emotion Recognition

Speech emotion recognition cross language families: Mandarin vs. western languages

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Cross-acoustic transfer learning for sound event classification

Emotion Classification from Noisy Speech - A Deep Learning Approach

TL;DR

Abstract

Authors

References36 items

Progressive Neural Networks for Transfer Learning in Emotion Recognition

Speech emotion recognition cross language families: Mandarin vs. western languages

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Cross-acoustic transfer learning for sound event classification

Emotion Classification from Noisy Speech - A Deep Learning Approach

Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression

An experimental study of speech emotion recognition based on deep convolutional neural networks

Transfer learning method using multi-prediction deep Boltzmann machines for a small scale dataset

Transfer learning using computational intelligence: A survey

Deep Neural Network Approaches to Speaker and Language Recognition

Speech emotion recognition using deep neural network and extreme learning machine

A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet)

A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

EMOVO Corpus: an Italian Emotional Speech Database

Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks

Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

Synthesized speech for model training in cross-corpus recognition of human emotion

Learning emotion-based acoustic features with deep belief networks

A Survey on Transfer Learning

Binary coding of speech spectrograms using a deep auto-encoder

Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies

Why Does Unsupervised Pre-training Help Deep Learning?

IEMOCAP: interactive emotional dyadic motion capture database

Representational Power of Restricted Boltzmann Machines and Deep Belief Networks

A Fast Learning Algorithm for Deep Belief Nets

Training Products of Experts by Minimizing Contrastive Divergence

A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space

Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method with Emotion Profiles

A brief survey on deep belief networks and introducing a new object oriented toolbox ( DeeBNet V 3 . 0 )

The Automatic Recognition of Emotions in Speech

Cross-Corpus Classification of Realistic Emotions - Some Pilot Experiments

Unsupervised feature learning via sparse hierarchical representations

The INTERSPEECH 2009 emotion challenge

This PDF file includes: Materials and Methods

A database of German emotional speech

Surrey audio-visual expressed emotion(savee) database

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