Research On Adaption Technique In Continuous Speech Keyword Spotting System

Automatic speech recognition is used more and more widely in people's life, which is categorized into continuous speech recognition and keyword spotting. Compared with continuous speech recognition, keyword spotting has advantage in increasing the naturalness of the dialogue. It is due to the user's meaning is understood by catching the keywords with important information of his utterance, while there is no need to recognize every word accurately. Keyword spotting is also a good solution for problems of tongue, such as non-standard, incoherence, etc.When there are many differences between the speeches for training and the speeches for testing, the performance of the system is greatly degraded. Adaption technique can reduce the gap between system model and speakers by adjusting the parameters of the system using a few speeches from the speakers, which increases the recognition rates.In this thesis, we focus on the application of speaker adaption technique and speaker normalization technique in keyword spotting system for the following aspects:1. A baseline system of keyword spotting based on Continue Hidden Markov Model (CHMM) is constructed. We discuss the design of baseline system in detail, which includes speech pretreatment, feature extract, acoustic models establishing and training, keyword detection, and keyword verification, etc. Also we evaluate the baseline system and bring forward the necessity of adding adaption module in baseline system.2. Both the speaker adaption technique and speaker normalization technique are investigated, and then the idea of combining the two techniques is brought forward. Experimental results indicate that the trained model is more independent after adding speaker normalization technique in the training, and the adaption based on this model could achieve higher recognition rates. Comparation and validation of the combination between several speaker normalization methods and speaker adaption methods are done. We select the scheme of combining SAT and CMLLR.