Noise robust speech emotion recognition with signal-to-noise ratio
  adapting speech enhancement
Yu-Wen Chen Julia Hirschberg Yu Tsao
Speech emotion recognition (SER) often experiences reduced performance due to
background noise. In addition, making a prediction on signals with only
background noise could undermine user trust in the system. In this study, we
propose a Noise Robust Speech Emotion Recognition system, NRSER. NRSER employs
speech enhancement (SE) to effectively reduce the noise in input signals. Then,
the signal-to-noise-ratio (SNR)-level detection structure and waveform
reconstitution strategy are introduced to reduce the negative impact of SE on
speech signals with no or little background noise. Our experimental results
show that NRSER can effectively improve the noise robustness of the SER system,
including preventing the system from making emotion recognition on signals
consisting solely of background noise. Moreover, the proposed SNR-level
detection structure can be used individually for tasks such as data selection.