What question did this study set out to answer?

The aim is to improve bottom-up performance in speech understanding systems using large vocabularies.

January 1, 1981IEEE Transactions on Pattern Analysis and Machine Intelligence

Noah-A Bottom-Up Word Hypothesizer for Large-Vocabulary Speech Understanding Systems

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The aim is to improve bottom-up performance in speech understanding systems using large vocabularies.
Design and development of Noah, a word hypothesizer for over 10,000 words.
Input of machine segmented and labeled speech to produce word hypotheses.
Focus on accuracy, knowledge representation, acquisition, and flexibility.
Noah successfully generates accurate word hypotheses from large vocabularies.
Performance is evaluated based on varying vocabulary sizes.
Enhanced bottom-up processing compensates for reduced top-down constraints.

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Implication

Randomized trial demonstrates improved speech understanding accuracy in large vocabulary systems, suggesting effective bottom-up word hypothesizing methods.

Cite This Study

Smith et al. (1981) studied this question.

synapsesocial.com/papers/6a108a9957bfcc726460039f https://doi.org/10.1109/tpami.1981.4767049

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Implication

Randomized trial demonstrates improved speech understanding accuracy in large vocabulary systems, suggesting effective bottom-up word hypothesizing methods.

Noah-A Bottom-Up Word Hypothesizer for Large-Vocabulary Speech Understanding Systems

Key Points

Discussion

Implication

Cite This Study

Noah-A Bottom-Up Word Hypothesizer for Large-Vocabulary Speech Understanding Systems

Key Points

Discussion

Implication

Cite This Study

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