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The current paper examines influences of speech rate on Fujisaki model parameters based on read speech from the BonnTempo-Corpus containing productions by 12 native speakers of German at five different intended tempo levels (very slow, slow, normal, fast, fastest possible).
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New AMs were obtained by doing a one-pass Viterbi training based on the native AMs with 6 hours of non-native read speech from the JASMIN corpus.
However, a series of recent experiments on corpora of recorded (read) speech and spontaneous (elicited) speech suggest that it is indeed possible to model human accent strategies with fair success (80 98% correct) for unrestricted text with only the tools for automatic text analysis currently available.
In this application, a feedback on similarity to fluent read speech would help speaker improve their level of oratorical skills.
Baseline triphone models were trained on 42 hours of native read speech from the CGN corpus [47].
Therefore, we also experimented with non-native monophone AMs trained on the same non-native read speech.
The speech data are based on two corpora; i.e., WSJCAM0 [29], which is a British English version of WSJ, and MC-WSJ [30], which consists of read speech recorded in a meeting room with a distant microphone array.
In a study on read and spontaneous speech produced by non-native speakers of Dutch [12], we found that extemporaneous speech contains many more filled pauses and disfluencies than read speech.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com