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Speech-based depression detection has gained importance in recent years, but most research has used relatively quiet conditions or examined a single corpus per study. Little is thus known about the robustness of speech cues in the wild. This study compares the effect of noise and reverberation on depression prediction using 1 ) standard mel-frequency cepstral coefficients (MFCCs), and 2) features...
Reverberation is a phenomenon observed in almost all enclosed environments. Human listeners rarely experience problems in comprehending speech in reverberant environments, but automatic speech recognition (ASR) systems often suffer increased error rates under such conditions. In this work, we explore the role of robust acoustic features motivated by human speech perception studies, for building ASR...
While human listeners can understand speech in reverberant conditions, indicating that the auditory system is robust to such degradations, reverberation leads to high word error rates for automatic speech recognition (ASR) systems. In this work, we present robust acoustic features motivated by human speech perception for use in a convolutional deep neural network (CDNN)-based acoustic model for recognizing...
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