  This paper integrates a voice activity detection (VAD) function with
end-to-end automatic speech recognition toward an online speech interface and
transcribing very long audio recordings. We focus on connectionist temporal
classification (CTC) and its extension of CTC/attention architectures. As
opposed to an attention-based architecture, input-synchronous label prediction
can be performed based on a greedy search with the CTC (pre-)softmax output.
This prediction includes consecutive long blank labels, which can be regarded
as a non-speech region. We use the labels as a cue for detecting speech
segments with simple thresholding. The threshold value is directly related to
the length of a non-speech region, which is more intuitive and easier to
control than conventional VAD hyperparameters. Experimental results on
unsegmented data show that the proposed method outperformed the baseline
methods using the conventional energy-based and neural-network-based VAD
methods and achieved an RTF less than 0.2. The proposed method is publicly
available.
