Investigating the Important Temporal Modulations for Deep-Learning-Based Speech Activity Detection

Abstract

We describe a learnable modulation spectrogram feature for speech activity detection (SAD). Modulation features capture the temporal dynamics of each frequency subband. We compute learnable modulation spectrogram features by first calculating the log-mel spectrogram. Next, we filter each frequency subband with a bandpass filter that contains a learnable center frequency. The resulting SAD system was evaluated on the Fearless Steps Phase-04 SAD challenge. Experimental results showed that temporal modulations around the 4–6 Hz range are crucial for deep-learning-based SAD. These experimental results align with previous studies that found slow temporal modulation to be most important for speech-processing tasks and speech intelligibility. Additionally, we found that the learnable modulation spectrogram feature outperforms both the standard log-mel and fixed modulation spectrogram features on the Fearless Steps Phase-04 SAD test set.