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Recognition by Indexing and Sequencing (RISq) is a general-purpose example-based method for classification of temporal vector sequences. The authors developed an advanced version of RISq and applied it to speech recognition, a task most commonly performed with Hidden Markov Models (HMMs) or Dynamic Time Warping (DTW). RISq is substantially different from both these methods and presents several advantages over them: robust recognition can be achieved using only a few samples from the input sequence and training can be carried out with one or more examples per class. This enables much faster training and also allows to recognize speech with a variety of accents.
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