Research On Pruning Algorithm For Speech Recognition

Speech is the most efficient way for human-beings’communication. For using speech input instead of traditional keyboard or touch screen input will make user feeling much more convenient, using speech recognition technology into human-computer interaction has been a cutting-edge research, In other hand, with rapid development of mobile terminals and mobile Internet, the speech recognition technology based on mobile terminals will become a new hotspot.Compared to proven speech recognition technique, the mobile terminal based speech recognition technology faced much more technical challenges. It’s mainly because of the mobile terminals’hardware is limited, which caused two problems:Firstly, the real-time performance cannot be met when using traditional recognition technology as the speech input system’s core algorithm; Secondly, high accuracy speech recognition system requires language model support, generally is statistical n-gram model, which is difficult to be used in the mobile terminal application for the larger storage space requirement. Current the researches on the mobile terminal based speech recognition technology are focused on the speeding up computation and storage compression.According to those problems, we proposed and implement an effective location based pruning algorithm. The mainly work is as follows:1. Pruning algorithm based on finite-state grammar recognition systemFirstly we researched on isolated word recognition system and fixed sentence recognition system, which are basic speech recognition systems and have the sampler recognition network than large vocabulary continuous speech recognition system. After lots of work, we proposed an algorithm to pruning network with user’s location information and used large number of experiments to prove the effectiveness of the algorithm.2. Pruning algorithm based on continuous speech recognition systemBased on finite-state recognition network pruning algorithm research, we proposed an algorithm which uses language model look-ahead tree structure and user’s location information to solve the same problem in continuous speech recognition system. By comparing the experiments among different systems we prove that the algorithm can effectively enhance the system’s recognition accuracy and speed performance.