  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.

Audio and Speech Processing;Machine Learning;Sound;Signal Processing