Hierarchical Multitream Posterior Based Speech recognition …分层多流后语音识别….ppt

9/21/2005 Hierarchical Approach for Spotting Keywords gsggdgdfsgdfsg Hierarchical Approach for SpottingKeywords from an Acoustic Stream Supervisor: Professor Raimo Kantola Instructor: Professor Hynek Hermansky, IDIAP Research Institute Introduction to the thesis Existing keyword spotting approaches are usually based on speech recognition techniques Growing apart from the original problem can lead to drawbacks, like lack of generality Another approach is presented and studied, where only the target sounds of the keyword are looked for To study and formulate this approach was my work at IDIAP Research Institute, 3/2005 - 8/2005 Objective ot the thesis: to see how far can we go without using hidden Markov models and dynamic programming techniques Outline Introduction to keyword spotting 4 - 7 Motivation for this work 8 Steps of hierarchical processing 9 - 14 Experiments 15 - 20 Conclusions 21 Keyword Spotting Keyword Spotting (KWS) aims at finding only certain words while rejecting the rest (hypothesis – test) Finding only certain, rare and high-information-valued words is feasible approach in for example voice command driven applications or multimedia indexing Performance measures for keyword spotting The possible events in keyword spotting are hit, false alarm and miss The performance is evaluated by presenting the detection rate as function of the false alarm rate This yields the receiver operating charasteristics (ROC) curve Average detection rate in 0-10 false alarms per hour is called figure of merit (FOM) [Roh89] LVCSR / HMM based approaches Typical large vocabulary continuous speech recognition (LVCSR) / hidden Markov model (HMM) based KWS approaches model both keywords and non-keywords (background or garbage) Keywords are searched by using dynamic programming techniques LVCSR / HMM based approaches vs. hypothesis test approach Motivation for this work Typical LVCSR / HMM based approaches require garbage model for Viterbi dynamic programmi