  Teaching with the cooperation of expert teacher and assistant teacher, which
is the so-called "double-teachers classroom", i.e., the course is giving by the
expert online and presented through projection screen at the classroom, and the
teacher at the classroom performs as an assistant for guiding the students in
learning, is becoming more prevalent in today's teaching method for K-12
education. For monitoring the teaching quality, a microphone clipped on the
assistant's neckline is always used for voice recording, then fed to the
downstream tasks of automatic speech recognition (ASR) and neural language
processing (NLP). However, besides its voice, there would be some other
interfering voices, including the expert's one and the student's one. Here, we
propose to extract the assistant' voices from the perspective of sound event
detection, i.e., the voices are classified into four categories, namely the
expert, the teacher, the mixture of them, and the background. To make
frame-level identification, which is important for grabbing sensitive words for
the downstream tasks, a multi-scale temporal convolution neural network is
constructed with stacked dilated convolutions for considering both local and
global properties. These features are concatenated and fed to a classification
network constructed by three linear layers. The framework is evaluated on
simulated data and real-world recordings, giving considerable performance in
terms of precision and recall, compared with some classical classification
methods.
