# Non-linear frequency warping using constant-Q transformation for speech emotion recognition

arXiv: https://arxiv.org/abs/2102.04029 · HTML: https://mlsanity.com/paper/2102.04029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition](https://mlsanity.com/paper/1706.02901/) (2017)

## Built on by (most cited first, 2 of 2)

- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [Deep scattering network for speech emotion recognition](https://mlsanity.com/paper/2105.04806/) (2021)

## Abstract (arXiv)

In this work, we explore the constant-Q transform (CQT) for speech emotion recognition (SER). The CQT-based time-frequency analysis provides variable spectro-temporal resolution with higher frequency resolution at lower frequencies. Since lower-frequency regions of speech signal contain more emotion-related information than higher-frequency regions, the increased low-frequency resolution of CQT makes it more promising for SER than standard short-time Fourier transform (STFT). We present a comparative analysis of short-term acoustic features based on STFT and CQT for SER with deep neural network (DNN) as a back-end classifier. We optimize different parameters for both features. The CQT-based features outperform the STFT-based spectral features for SER experiments. Further experiments with cross-corpora evaluation demonstrate that the CQT-based systems provide better generalization with out-of-domain training data.