New uses for the N-Best sentence hypotheses within the BYBLOS speech recognition system

Schwartz, Richard; Austin, S.; Kubala, Francis; Makhoul, John; Nguyen, Long; Placeway, Paul; Zavaliagkos, G.

doi:10.1109/icassp.1992.225987

Cited by 36 publications

(17 citation statements)

References 10 publications

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“…However, if the language-understanding component is to provide an additional knowledge source to help in choosing the "right answer," it must have access to multiple hypotheses from the recognizer. The N-best interface (21,22)e has proved to be a convenient vehicle for such experimentation: it makes it easy to interface the recognition and understanding components, it requires no change to either component, and it permits off-line exploration of a large search space. Also, there has recently been renewed interest in word networks as a compact representation of a large set of recognition hypotheses (23,24).…”

Section: Interfacing Speech and Languagementioning

confidence: 99%

The roles of language processing in a spoken language interface.

Hirschman¹

1995

Proc. Natl. Acad. Sci. U.S.A.

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This paper provides an overview of the colloquium's discussion session on natural language understanding, which followed presentations by M. Bates [Bates, M. (1995) Proc This paper provides an overview of the natural language understanding session at the Colloquium on HumanMachine Communication by Voice held by the National Academy of Sciences (NAS). The aim of the paper is to review the role that language understanding plays in spoken language systems and to summarize the discussion that followed the two presentations by Bates (1) and Moore (2). A number of questions were raised during the discussion, including whether a single system could provide both understanding and constraint, what the future role of discourse should be, how to evaluate performance on interactive systems, and whether we are moving in the right direction toward realizing the goal of interactive human-machine communication.Background: The ARPA Spoken Language Program

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Section: Interfacing Speech and Languagementioning

confidence: 99%

The roles of language processing in a spoken language interface.

Hirschman¹

1995

Proc. Natl. Acad. Sci. U.S.A.

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“…The top choice in the final list constitutes the speech recognition results reported below. This N-best strategy [3,4] permits the use of otherwise computationally prohibitive models by greatly reducing the search space to a few (N=20-100) word sequences. It has enabled us to use cross-word-bonndary triphone models and trigram language models with ease.…”

Section: Byblosmentioning

confidence: 99%

Comparative experiments on large vocabulary speech recognition

Kubala

Anastasakos²,

Makhoul³

et al.

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper describes several key experiments in large vocabulay speech recognition. We demonstrate that, counter to our intuitions, given a fixed amount of training speech, the number of training speakers has little effect on the accuracy. We show how much speech is needed for speaker-independent (SI) recognition in order to achieve the same performance as speakerdependent (SD) recognition. We demonstrate that, though the N-Best Paradigm works quite well up to vocabularies of 5,000 words, it begins to break down with 20,000 words and long sentences. We compare the performance of two feature preprocessing algorithms for microphone independence and we describe a new microphone adaptation algorithm based on selection among several codebook transformations.

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“…These weights were optimized on the development test set automaticaUy using the N-best lists [4]. Optimization of these weights reduced the word error by 0.4%.…”

Section: Weight Optimi~ationmentioning

confidence: 99%