Speech Recognition Using ANNs
Given all the difficulties presented in Chapter 1, Automatic Speech Recognition (ASR) remains a challenging problem in pattern recognition. After half a century of research, the performance currently achieved by state of the art systems is not yet at the level of a mature technology. Over the years, many technological innovations have boosted the level of performance for more and more difficult tasks. Some of the most significant of these innovations include: (1) pattern matching approaches (e.g., DTW), (2) statistical pattern recognition (e.g., HMMs), (3) better use of a priori phonological knowledge, and (4) integration of syntactic constraints in Continuous Speech Recognition (CSR) algorithms. However, despite impressive improvements, performance on realistic (i.e., fairly unconstrained) tasks are still far too low for effective use. It seems likely that new technological breakthroughs will be required for the major performance improvement that will be required. Even if one assumes infinite computational power, an infinite storage and corresponding memory bandwidth, and an infinite amount of training data, it is still not certain that one could solve the ASR problem in a satisfactory way. It has also become clear that the use of higher level knowledge during the recognition process (or more generally, the efficient interaction between multiple knowledge sources) is required to overcome the limitations of current ASR systems.
KeywordsSpeech Recognition Input Pattern Automatic Speech Recognition Hide Unit Dynamic Time Warping
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