Building Natural Language Generation Systems (Studies in - download pdf or read online

By Ehud Reiter

ISBN-10: 0521620368

ISBN-13: 9780521620369

This booklet explains tips on how to construct traditional Language new release (NLG) systems--computer software program structures that instantly generate comprehensible texts in English or different human languages. NLG structures use wisdom approximately language and the applying area to immediately produce files, reviews, motives, support messages, and different kinds of texts. The publication covers the algorithms and representations had to practice the middle initiatives of rfile making plans, microplanning, and floor cognizance, utilizing a case examine to teach how those parts healthy jointly. it truly is crucial examining for researchers attracted to NLP, AI, and HCI; and for builders attracted to complicated document-creation expertise.

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Extra info for Building Natural Language Generation Systems (Studies in Natural Language Processing)

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A head-mount electret condenser microphone was used for recording (Sennheiser HMD410). The recordings were made with a sampling frequency of 16 kHz, 16 bits/sample, monaural. The speakers were asked to speak standard Japanese dialect. The Kanji representations of the words were presented on screen, and subjects spoke the words for recording. Accents were validated off-line. We validated that all words within the same group were of the same accent. Re-recording was done with some of the words with unclear or non-conventional accents.

42 5 Examples of Noise-Degraded Speech Intelligibility Measurements 100 female Chance-Adjusted Percentage Correct Response [%] male 80 60 40 20 0 -15 -10 -5 0 5 10 15 No noise 20 SNR [dB] Fig. 11 Comparison of DRT scores of speech mixed with white noise by speaker gender (average) 100 female Chance-Adjusted Percentage Correct Response [%] male 80 60 40 20 0 -15 -10 -5 0 5 10 15 20 No noise SNR [dB] Fig. 12 Comparison of DRT scores of speech mixed with white noise by speaker gender (voicing) 3. Much less differences by features are seen with pseudo-speech and babble noise compared to white noise.

In real space, the target speech and the noise were generated from loudspeakers placed at actual source positions. , a mixture of speech recorded in a busy cafeteria. The noise sample was obtained from the Signal Processing Information Base (SPIB) page [19] of Rice University. 98 kHz with 16 bits per sample, and was down-sampled to 16 kHz. The target speech was collected from one female speaker. Ideally, we would have liked to use more speakers, including male speakers. However, we have already shown that, at least for simple additive noise, little difference is observed between intelligibility for male and female speakers [7].

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Building Natural Language Generation Systems (Studies in Natural Language Processing) by Ehud Reiter


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