# Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation

arXiv: https://arxiv.org/abs/2207.03334 · HTML: https://mlsanity.com/paper/2207.03334/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal distillation, one of 12 directions in Knowledge distillation.
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Placed in

- Knowledge distillation / [Multimodal distillation](https://mlsanity.com/distill/multimodal-distillation/)

## Builds on (most cited first, 13 of 13)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings](https://mlsanity.com/paper/2104.03502/) (2021)
- [Large-scale Self-Supervised Speech Representation Learning for Automatic Speaker Verification](https://mlsanity.com/paper/2110.05777/) (2021)
- [Fine-tuning wav2vec2 for speaker recognition](https://mlsanity.com/paper/2109.15053/) (2021)
- [Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve Multimodal Speech Emotion Recognition](https://mlsanity.com/paper/2008.06682/) (2020)
- [Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages](https://mlsanity.com/paper/2012.12121/) (2020)
- [First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs](https://mlsanity.com/paper/1408.2873/) (2014)
- [Representation learning through cross-modal conditional teacher-student training for speech emotion recognition](https://mlsanity.com/paper/2112.00158/) (2021)
- [Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Dataset](https://mlsanity.com/paper/2110.04425/) (2021)
- [Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions](https://mlsanity.com/paper/2002.01323/) (2020)
- [Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition](https://mlsanity.com/paper/2201.11826/) (2022)
- [Leveraging Acoustic Cues and Paralinguistic Embeddings to Detect Expression from Voice](https://mlsanity.com/paper/1907.00112/) (2019)

## Built on by (most cited first, 5 of 5)

- [MAEEG: Masked Auto-encoder for EEG Representation Learning](https://mlsanity.com/paper/2211.02625/) (2022)
- [Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression](https://mlsanity.com/paper/2306.06760/) (2023)
- [Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis](https://mlsanity.com/paper/2312.16180/) (2023)
- [Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis](https://mlsanity.com/paper/2303.03177/) (2023)
- [Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions](https://mlsanity.com/paper/2503.22711/) (2025)

## Abstract (arXiv)

Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years. While accurate estimation of activation and dominance from speech seem to be possible, the same for valence remains challenging. Previous research has shown that the use of lexical information can improve valence estimation performance. Lexical information can be obtained from pre-trained acoustic models, where the learned representations can improve valence estimation from speech. We investigate the use of pre-trained model representations to improve valence estimation from acoustic speech signal. We also explore fusion of representations to improve emotion estimation across all three emotion dimensions: activation, valence and dominance. Additionally, we investigate if representations from pre-trained models can be distilled into models trained with low-level features, resulting in models with a less number of parameters. We show that fusion of pre-trained model embeddings result in a 79% relative improvement in concordance correlation coefficient CCC on valence estimation compared to standard acoustic feature baseline (mel-filterbank energies), while distillation from pre-trained model embeddings to lower-dimensional representations yielded a relative 12% improvement. Such performance gains were observed over two evaluation sets, indicating that our proposed architecture generalizes across those evaluation sets. We report new state-of-the-art "text-free" acoustic-only dimensional emotion estimation CCC values on two MSP-Podcast evaluation sets.