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Exploring deep learning architectures for automatically grading non-native spontaneous speech.

, , , and . ICASSP, page 6140-6144. IEEE, (2016)

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Generalized Perceptual Features for Vocalization Analysis Across Multiple Species., , , , and . ICASSP (1), page 253-256. IEEE, (2006)Perceptually motivated wavelet packet transform for bioacoustic signal enhancement, , and . Journal of the Acoustical Society of America, 124 (1): 316-327 (2008)Noise and Metadata Sensitive Bottleneck Features for Improving Speaker Recognition with Non-Native Speech Input., , , , , and . INTERSPEECH, page 3648-3652. ISCA, (2016)Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech., , , , , , , , and . ASRU, page 338-345. IEEE, (2015)Exploring deep learning architectures for automatically grading non-native spontaneous speech., , , and . ICASSP, page 6140-6144. IEEE, (2016)Performance of a trialogue-based prototype system for English language assessment for young learners., , , , , , and . WOCCI, page 79-84. ISCA, (2014)Automated Speech Recognition Technology for Dialogue Interaction with Non-Native Interlocutors., , , , , and . SIGDIAL Conference, page 134-138. The Association for Computer Linguistics, (2015)Stress and Emotion Classification using Jitter and Shimmer Features., , , , , , and . ICASSP (4), page 1081-1084. IEEE, (2007)End-to-End Neural Network Based Automated Speech Scoring., , , and . ICASSP, page 6234-6238. IEEE, (2018)The influence of automatic speech recognition accuracy on the performance of an automated speech assessment system., , and . SLT, page 294-299. IEEE, (2014)