  This article presents a whisper speech detector in the far-field domain. The
proposed system consists of a long-short term memory (LSTM) neural network
trained on log-filterbank energy (LFBE) acoustic features. This model is
trained and evaluated on recordings of human interactions with
voice-controlled, far-field devices in whisper and normal phonation modes. We
compare multiple inference approaches for utterance-level classification by
examining trajectories of the LSTM posteriors. In addition, we engineer a set
of features based on the signal characteristics inherent to whisper speech, and
evaluate their effectiveness in further separating whisper from normal speech.
A benchmarking of these features using multilayer perceptrons (MLP) and LSTMs
suggests that the proposed features, in combination with LFBE features, can
help us further improve our classifiers. We prove that, with enough data, the
LSTM model is indeed as capable of learning whisper characteristics from LFBE
features alone compared to a simpler MLP model that uses both LFBE and features
engineered for separating whisper and normal speech. In addition, we prove that
the LSTM classifiers accuracy can be further improved with the incorporation of
the proposed engineered features.
