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1932-121X continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative978-3-031-01429-1978-3-031-02557-0Series ISSN 1932-121X Series E-ISSN 1932-1678
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Statistical Speech Recognition: A Tutorial,ing tool for characterizing acoustic features in speech. The purpose of this chapter is to set up the context in which HMM parameter learning and discriminative learning in particular, will be introduced.
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Selected Experimental Results, minimum classification error (MCE) training method on both small-vocabulary, well-controlled benchmark tests such as TIDIGITS, and on large-vocabulary, real-world speech recognition tasks such as commercial telephony large-vocabulary ASR (LV-ASR) applications. We show that the GT-based discriminati
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978-3-031-01429-1Springer Nature Switzerland AG 2008
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Sustainable Design for Global Equilibriuming tool for characterizing acoustic features in speech. The purpose of this chapter is to set up the context in which HMM parameter learning and discriminative learning in particular, will be introduced.
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https://doi.org/10.1007/978-3-030-94818-4HMMs). These are: maximum mutual information (MMI), minimum classification error (MCE), and minimum phone error/minimum word error (MPE/MWE). We also compare our unified form of these objective functions with another popular unified form in the literature.