  In this work, we address a novel, but potentially emerging, problem of
discriminating the natural human voices and those played back by any kind of
audio devices in the context of interactions with in-house voice user
interface. The tackled problem may find relevant applications in (1) the
far-field voice interactions of vocal interfaces such as Amazon Echo, Google
Home, Facebook Portal, etc, and (2) the replay spoofing attack detection. The
detection of loudspeaker emitted speech will help avoid false wake-ups or
unintended interactions with the devices in the first application, while
eliminating attacks involve the replay of recordings collected from enrolled
speakers in the second one. At first we collect a real-world dataset under
well-controlled conditions containing two classes: recorded speeches directly
spoken by numerous people (considered as the natural speech), and recorded
speeches played back from various loudspeakers (considered as the loudspeaker
emitted speech). Then from this dataset, we build prediction models based on
Deep Neural Network (DNN) for which different combination of audio features
have been considered. Experiment results confirm the feasibility of the task
where the combination of audio embeddings extracted from SoundNet and VGGish
network yields the classification accuracy up to about 90%.
