  This paper considers the impact of automatic segmentation on the
fully-automatic, semi-supervised training of automatic speech recognition (ASR)
systems for five-lingual code-switched (CS) speech. Four automatic segmentation
techniques were evaluated in terms of the recognition performance of an ASR
system trained on the resulting segments in a semi-supervised manner. The
system's output was compared with the recognition rates achieved by a
semi-supervised system trained on manually assigned segments. Three of the
automatic techniques use a newly proposed convolutional neural network (CNN)
model for framewise classification, and include a novel form of HMM smoothing
of the CNN outputs. Automatic segmentation was applied in combination with
automatic speaker diarization. The best-performing segmentation technique was
also tested without speaker diarization. An evaluation based on 248 unsegmented
soap opera episodes indicated that voice activity detection (VAD) based on a
CNN followed by Gaussian mixture modelhidden Markov model smoothing
(CNN-GMM-HMM) yields the best ASR performance. The semi-supervised system
trained with the resulting segments achieved an overall WER improvement of 1.1%
absolute over the system trained with manually created segments. Furthermore,
we found that system performance improved even further when the automatic
segmentation was used in conjunction with speaker diarization.
