We describe in this paper the experiences of the Johns Hopkins University team during the inaugural DIHARD diarization evaluation. This new task provided microphone recordings in a variety of difficult conditions and challenged researchers to fully consider all speaker activity, without the currently typical practices of unscored collars or ignored overlapping speaker segments. This paper explores several key aspects of currently state-of-the-art diarization methods, such as training data selection, signal bandwidth for feature extraction, representations of speech segments (i-vector versus x-vector), and domainadaptive processing. In the end, our best system clustered xvector embeddings trained on wideband microphone data followed by Variational-Bayesian refinement, and a speech activity detector specifically trained for this task with in-domain data was found to be the best performing. After presenting these decisions and their final result, we discuss lessons learned and remaining challenges within the lens of this new approach to diarization performance measurement.

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In this paper, we apply x-vectors to the task of spoken language recognition. This framework consists of a deep neural network that maps sequences of speech features to fixed-dimensional embeddings, called x-vectors. Longterm language characteristics are captured in the network by a temporal pooling layer that aggregates information across time. Once extracted, x-vectors utilize the same classification technology developed for i-vectors. In the 2017 NIST language recognition evaluation, x-vectors achieved excellent results and outperformed our state-ofthe-art i-vector systems. In the post-evaluation analysis presented here, we experiment with several variations of the x-vector framework, and find that the best performing system uses multilingual bottleneck features, data augmentation, and a discriminative Gaussian classifier.

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