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In this paper, we present and evaluate an approach to incremental dialogue act (DA) segmentation and classification. Our approach utilizes prosodic, lexico-syntactic and contextual features, and achieves an encouraging level of performance in offline corpus-based evaluation as well as in simulated human-agent dialogues. Our approach uses a pipeline of sequential processing steps, and we investigate the contribution of different processing steps to DA segmentation errors. We present our results using both existing and new metrics for DA segmentation. The incremental DA segmentation capability described here may help future systems to allow more natural speech from users and enable more natural patterns of interaction.

Despite the great achievements of the intelligent diagnosis methods of rotating machinery based on being data-driven, it still suffers from the problem of scarce labeled data. Therefore, this paper focuses on developing a data augmentation method of few-shot learning for fault diagnosis under small sample size conditions. Firstly, we developed the latent optimized stable generative adversarial network to adaptively augment the small sample size data without prior knowledge. Furthermore, penalty terms based on the distance metric for differences in distributions are adopted to constrain the optimization objective of the model. And self-attention and spectral normalization are applied in the model to stabilize the training process. Then, supervised classifier training is conducted based on the augmented sample set. Comparative analysis of the frequency spectrum verified the authenticity and reliability of the generated samples. Finally, the performance of the proposed method is validated with a comparative study on three cases of rolling bearing fault diagnosis experiments. The average accuracy can achieve 99.71%, 99.7%, and 96.27% in 10-shot sample fault diagnosis.

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