Lossless compression is a classic technique for reducing data storage and transmission requirements. Asymmetric Numeral Systems (ANS) is a high-throughput, high-ratio lossless compression algorithm, but it lacks effective support for multi-byte data and cross-platform compatibility. To address this issue, we propose an Adaptive Data Mapping (ADM) scheme, which maps multi-byte integer data into single-byte space based on the data’s characteristics, improving the compression ratio of ANS while maintaining low encoding redundancy. We also optimize the ADM algorithm and the ANS encoder for GPU and CPU architectures, respectively, and combine them to create an efficient and portable ANS encoding method for multi-byte integer data, called MANS. Experimental results show that MANS improves compression ratios by an average of 1.24 ×, achieves 870.27MB/s throughput on CPUs, and delivers up to 288.45 × and 135.86 × speedups on an NVIDIA A100 and an AMD MI210 GPU compared to the CPU version—demonstrating its efficiency and portability across platforms.

A new, data-driven approach to deriving overlapping articulatory-feature based HMMs for speech recognition is presented in this paper. This approach uses speech data from University of Wisconsin's Microbeam X-ray Speech Production Database. Regression tree models were created for constructing HMMs. Use of actual articulatory data improves upon our previous rule-based feature overlapping system. The regression trees allow construction of the HMM topology for an arbitrary utterance given its phonetic transcription and some prosodic information. Experimental results in ASR show preliminary success of this approach. 1.