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.

本文探讨了基于 constant-Q 转换（CQT）的短时语音情感识别（SER），使用 CQT 的低频分辨率优于标准短时傅里叶变换（STFT）的高频分辨率，经过比较分析，使用深度神经网络（DNN）作为后端分类器对基于 STFT 和 CQT 的短期声学特征进行了参数优化和实验，结果表明使用 CQT 的特征优于 STFT 的光谱特征，并且交叉语料库评估实验表明使用 CQT 的系统对于域外训练数据具有更好的泛化能力。