ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing
DOI: 10.1109/icassp.1986.1168806
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Plan refinement in a knowledge-based system for automatic speech recognition

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Cited by 6 publications
(2 citation statements)
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“…The next step is the development of feature detectors that can identify and extract these cues from the speech signal. The expert's decision-making process is encapsulated in a set of rules for classification (Lamel & Zue, 1984;Espy-Wilson, 1986De Mori & Lam, 1986;Zue & Lamel, 1986;Bulot & Nocera, 1989). The main problem here is that spectrogram-reading knowledge is tied with the ability of experts to see "events" in the spectrogram and determine the corresponding sounds or phones.…”
Section: Feature Extraction Componentmentioning
confidence: 99%
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“…The next step is the development of feature detectors that can identify and extract these cues from the speech signal. The expert's decision-making process is encapsulated in a set of rules for classification (Lamel & Zue, 1984;Espy-Wilson, 1986De Mori & Lam, 1986;Zue & Lamel, 1986;Bulot & Nocera, 1989). The main problem here is that spectrogram-reading knowledge is tied with the ability of experts to see "events" in the spectrogram and determine the corresponding sounds or phones.…”
Section: Feature Extraction Componentmentioning
confidence: 99%
“…Still, the identification, development and formulation of the rules is performed manually by utilizing the expert. Thus, there is a strong reliance on the ability of the expert and the rule developer to identify and extract correctly the events in the speech pattern (Carbonell, et al, 1984;Carbonell, et al, 1986;Green & Wood, 1986;Mizoguchi, et al, 1986;De Mori & Lam, 1986;Stern, et al, 1986;Fohr et al, 1989). The main disadvantage of this technique is that we still need to formulate hundreds of rules, which are generally interrelated and very difficult to manage.…”
Section: Classification Componentmentioning
confidence: 99%