Regression models are employed in lossless compression of time series data, by storing the residual of each point, known as regression encoding. Owing to value fluctuation, the regression residuals could be large and thus occupy huge space. It is worth noting that compared to the fluctuating values, time intervals are often regular and easy to compress, especially in the IoT scenarios where sensor data are collected in a preset frequency. In this sense, there is a trade-off between storing the regular timestamps and fluctuating values. Intuitively, rather than in time order, we may exchange the data points in the series such that the nearby ones have both smoother timestamps and values, leading to lower residuals. In this paper, we propose to reorder the time series data for better regression encoding. Rather than recomputing from scratch, efficient updates of residuals after moving some points are devised. The experimental comparison over various real-world datasets, either public or collected by our industrial partners, illustrates the superiority of the proposal in compression ratio. The method, REGression Encoding with Reordering (REGER), has now become an encoding method in an open-source time series database, Apache IoTDB.

Recent advances in cross-lingual text-to-speech (TTS) made it possible to synthesize speech in a language foreign to a monolingual speaker. However, there is still a large gap between the pronunciation of generated cross-lingual speech and that of native speakers in terms of naturalness and intelligibility. In this paper, a triplet training scheme is proposed to enhance the cross-lingual pronunciation by allowing previously unseen content and speaker combinations to be seen during training. Proposed method introduces an extra fine-tune stage with triplet loss during training, which efficiently draws the pronunciation of the synthesized foreign speech closer to those from the native anchor speaker, while preserving the non-native speaker’s timbre. Experiments are conducted based on a state-of-the-art baseline cross-lingual TTS system and its enhanced variants. All the objective and subjective evaluations show the proposed method brings significant improvement in both intelligibility and naturalness of the synthesized cross-lingual speech.