Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD tools like WebRTC VAD have generally relied on pause-based segmentation. Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connected by a very short pause that is difficult to detect by VAD. In this study, we propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus. We also propose a hybrid method that combines VAD and the above speech segmentation method. Experimental results revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods. The hybrid approach further improved the translation performance.

Department of Computer Science and Engineering Texas A&M University College Station, TX 77840, USA Abstract PyODDS is an end-to-end Py thon system for O utlier D etection with Database Support. It provides various outlier detection algorithms which meet the demands for users in different fields, with or without data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. Keywords: anomaly detection, end-to-end system, outlier detection, deep learning, machine learning, data mining, full stack system, data visualization 1. Introduction Outliers refer to the objects with patterns or behaviors that are significantly rare and different with the rest of majorities.