International Conference on Acoustics, Speech, and Signal Processing · 2003 · 97 citations · 6 references
ComputingComputer ScienceComputational LinguisticsNatural Language ProcessingPhoneticsLanguage ModelingLanguage ModelsSpeech RecognitionAccuracyCoarticulationPerplexitySpeaker-independent Speech RecognitionSpeech Recognition AccuracySpeech Recognition EvaluationSpeech UnderstandingWord Recognition Accuracy
A description is given of SPHINX an accurate large-vocabulary speaker-independent continuous speech recognition system. The authors have made several recent enhancements, including generalized triphone models, word duration modeling, function-phrase modeling, between-word coarticulation modeling, and corrective training. On the 997-word resource management task, SPHINX attained a word accuracy of 96% with a grammar (perplexity 60), and 82% without grammar (perplexity 997).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Pattern recognition in practice
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