Applying and Studying Speech Recognition to Digital Entertainment

[[abstract]]Virtual reality interfaces for simulation reality are being developed gradually in digital entertainment. Many user interfaces are being developed and applied to digital entertainment. Speech recognition systems have so far been impractical, due to poor recognition of pronunciation. However, increasing scientific and technological progress in this field has led to increasing interest in digital entertainment. The speech interface has become a new tool for digital entertainment in this generation. However, difficulties are still present in distinguishing speakers, or identifying speech from different speakers. This thesis presents a hybrid method of analyzing speech that is both speaker-independent and speaker-dependent. The most popular method of speech recognition is currently HMM (Hidden Markov Model). This model requires a huge database set up use to identify speech and distinguish among speakers. In small vocabulary speech recognition, DTW (Dynamic Time Warping) does not need large corpus, but effectively distinguishes speech with a small vocabulary. This thesis presents a hybrid approach that combines HMM with DTW. The weighting of the data processed by each method is determined and adjusted to achieve the best distinguishing rate that strengthens the recognition of short vocabulary and the ability of distinguishing ofthe speaker-independent