Research On An Embedded Speech Recognition System Based On HMM

Speech recognition is a technology which can auto convert the voice signal into te or commands by identifying and understanding. It has made a series of breakthrou after decades of exploration and research. As convenient means of human-compul interaction,speech recognition developed rapidly in the field of consumer electronics ai industrial control products recently. Speech recognition system with large vocabula recognition, good real-time and relatively high rates of correct is worth reseachin especially embedded speech recognition.Paper introduces the background and research status of speech recogniti technology firstly, and analyzes the technical indicators of project researched. The focus on the technology of the module based on the depth study of key modules recognition (voice preprocessing, feature parameters extraction, recognition algorithm Paper researches the pre-emphasis, framing, adding window, and endpoint detection (?) short-term energy and zero crossing rate for voice preprocessing; chooses MFCC feature parameters in the project after comparison between LPCC and MFCC.Then, paper introduces three classical recognition algorithms and analyzes t(?) hidden markov model (HMM) as the core algorithm. The number of the HMM mod state will influence the correct rate of recognition, as a result, paper improves the HM(?) model and recognition algorithm, and proposes a double templates matching. Paper bu(?) a speech recognition system using MFCC and improved HMM on the VC6.0 platfor(?) and the recognition rate is up to 90%, meeting the performance requirements of t(?) background project.Finally, paper chooses Windows CE operating system based on the research(?) existing embedded operating system, and succeeded in customizing and transplanting c the ARM 11 core developed board. Through cross-platform software developmen embedded development successfully built a good platform to achieve a large vocabulai speech recognition system under Windows CE 6.0 operating system. Experiment results show that the improvement on the system can achieve a better, real-time, larg vocabulary speech recognition system in the embedded system.