  Most current speech technology systems are designed to operate well even in
the presence of multiple active speakers. However, most solutions assume that
the number of co-current speakers is known. Unfortunately, this information
might not always be available in real-world applications. In this study, we
propose a real-time, single-channel attention-guided Convolutional Neural
Network (CNN) to estimate the number of active speakers in overlapping speech.
The proposed system extracts higher-level information from the speech spectral
content using a CNN model. Next, the attention mechanism summarizes the
extracted information into a compact feature vector without losing critical
information. Finally, the active speakers are classified using a fully
connected network. Experiments on simulated overlapping speech using WSJ corpus
show that the attention solution is shown to improve the performance by almost
3% absolute over conventional temporal average pooling. The proposed
Attention-guided CNN achieves 76.15% for both Weighted Accuracy and average
Recall, and 75.80% Precision on speech segments as short as 20 frames (i.e.,
200 ms). All the classification metrics exceed 92% for the attention-guided
model in offline scenarios where the input signal is more than 100 frames long
(i.e., 1s).

Audio and Speech Processing;Machine Learning;Sound