Maximum likelihood and minimum classification error factor analysis for automatic speech recognition
نویسندگان
چکیده
منابع مشابه
Maximum likelihood and minimum classification error factor analysis for automatic speech recognition
Hidden Markov models (HMMs) for automatic speech recognition rely on high dimensional feature vectors to summarize the short-time properties of speech. Correlations between features can arise when the speech signal is non-stationary or corrupted by noise. We investigate how to model these correlations using factor analysis, a statistical method for dimensionality reduction. Factor analysis uses...
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ژورنال
عنوان ژورنال: IEEE Transactions on Speech and Audio Processing
سال: 2000
ISSN: 1063-6676
DOI: 10.1109/89.824696