Acoustic model and adaptation for automatic speech recognition and ani
Automatic speech recognition (ASR) converts human speech to readable text. Acoustic model adaptation, also called speaker adaptation, is one of the most promising techniques in ASR for improving recognition accuracy. Adaptation works by tuning a general purpose acoustic http://www.dolphinsofficialnflstore.com/5-dan-carpenter-jersey-authentic-black-limited-cheap.html model to a specific one according to the person who is using it. Speaker adaptation can be categorized by Bayesianbased, transformationbased and model combinationbased methods. Model combinationbased speaker adaptation has been shown to have an advantage over the traditional Bayesianbased and transformationbased adaptation methods when the amount of adaptation speech is as small as a few seconds. However, model Reshad Jones Black Jersey combinationbased rapid speaker adaptation has not been widely used in practical applications since it requires large amounts of speakerdependent (SD) training data from multiple speakers. This research proposes a new technique, eigenclustering , to eliminate the need for large quantities of speakerlabeled training utterances so that model combinationbased adaptation can be started from much more Reshad Jones Authentic Jersey inexpensive speakerindependent (SI) data. Based on principal component analysis (PCA), this technique constructs an eigenspace using each utterance in the training set. This proposed adaptation method can not only improve human speech recognition directly, but also http://www.dolphinsofficialnflstore.com contribute to animal vocalization analysis and behavior studies potentially. Application to the field of bioacoustics is especially meaningful because the amount of collected animal vocalization data is often limited and therefore fast adaptation methods are naturally suitable.
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