PRAAT scripts to measure speed fluency and breakdown fluency in speech automatically

References

• Audhkhasi, K., Kandhway, K., Deshmukh, O. D., & Verma, A. (2009). Formant-based technique for automatic filled pause detection in spontaneous spoken English, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, (pp. 4857–4860). IEEE.
   Google Scholar
• Belsley, D. (1991). Conditioning Diagnostics: Collinearity and Weak Data in Regression. Wiley.
   Google Scholar
• Boersma, P., & Weenink, D. (2016). PRAAT: Doing phonetics by computer [Computer program]. Version 6.1.29. Retrieved 28 October 2019, from http://www.PRAAT.org/.
   Google Scholar
• Bosker, H. R., Pinget, A. F., Quené, H., Sanders, T., & De Jong, N. H. (2013). What makes speech sound fluent? The contributions of pauses, speed and repairs. Language Testing, 30(2), 159–175. https://doi.org/https://doi.org/10.1177/2F0265532212455394
   Web of Science ®Google Scholar
• Clark, H. H., & Fox Tree, J. E. (2002). Using uh and um in spontaneous speaking. Cognition, 84(1), 73–111. https://doi.org/https://doi.org/10.1016/s0010-0277(02)00017-3
   PubMed Web of Science ®Google Scholar
• De Boer, M. M., & Heeren, W. F. L. (2020). Cross-linguistic filled pause realization: The acoustics of uh and um in native Dutch and non-native English. Journal of the Acoustical Society of America, 148(6), 3612–3622. https://doi.org/https://doi.org/10.1121/10.0002871
   PubMed Web of Science ®Google Scholar
• De Jong, N. H. (2016). Predicting pauses in L1 and L2 speech: The effects of utterance boundaries and word frequency. International Review of Applied Linguistics in Language Teaching, 54(2), 113-132. https://doi.org/https://doi.org/10.1515/iral-2016-9993
   Google Scholar
• De Jong, N. H., & Wempe, T. (2009). PRAAT script to detect syllable nuclei and measure speech rate automatically. Behavior Research Methods, 41(2), 385–390. https://doi.org/https://doi.org/10.3758/BRM.41.2.385
   PubMed Web of Science ®Google Scholar
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/https://doi.org/10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• Foster, P., Tonkyn, A., & Wigglesworth, G. (2000). Measuring spoken language: A unit for all reasons. Applied Linguistics, 21(3), 354–375. https://doi.org/https://doi.org/10.1093/applin/21.3.354
   Web of Science ®Google Scholar
• Ginther, A., Dimova, S., & Yang, R. (2010). Conceptual and empirical relationships between temporal measures of fluency and oral english proficiency with implications for automated scoring. Language Testing, 27(3), 379–399. https://doi.org/https://doi.org/10.1177/0265532210364407
   Web of Science ®Google Scholar
• Hughes, V., Foulkes, P., & Wood, S. (2016a). Formant dynamics and durations of um improve the performance of automatic speaker recognition systems. Proceedings of the 16th Australasian Conference on Speech Science and Technology (ASSTA). Western Sydney University
   Google Scholar
• Hughes, V., Wood, S., & Foulkes, P. (2016b). Strength of forensic voice comparison evidence from the acoustics of filled pauses. International Journal of Speech, Language and the Law, 23(1), 99–132. https://doi.org/https://doi.org/10.1558/ijsll.v23i1.29874
   Web of Science ®Google Scholar
• Hulstijn, J. H., Schoonen, R., De Jong, N. H., Steinel, M. P., & Florijn, A. F. (2012). Linguistic competences of learners of Dutch as a second language at the B1 and B2 levels of speaking proficiency of the common European framework of reference for languages (CEFR). Language Testing, 29(2), 202–220. https://doi.org/https://doi.org/10.1177/0265532211419826
   Web of Science ®Google Scholar
• Iwashita, N., Brown, A., McNamara, T., & O’Hagan, S. (2008). Assessed levels of second language speaking proficiency: How distinct? Applied Linguistics, 29(1), 24–49. https://doi.org/https://doi.org/10.1093/applin/amm017
   Web of Science ®Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer.
   Google Scholar
• Kahng, J. (2014). Exploring utterance and cognitive fluency of L1 and L2 english speakers: Temporal measures and stimulated recall. Language Learning, 64(4), 809–854. https://doi.org/https://doi.org/10.1111/lang.12084
   Web of Science ®Google Scholar
• Kahng, J. (2018). The effect of pause location on perceived fluency. Applied Psycholinguistics, 39(3), 569–591. https://doi.org/https://doi.org/10.1017/S0142716417000534
   Web of Science ®Google Scholar
• Kang, O., Rubin, D., & Pickering, L. (2010). Suprasegmental measures of accentedness and judgments of language learner proficiency in oral english. The Modern Language Journal, 94(4), 554–566. https://doi.org/https://doi.org/10.1111/j.1540-4781.2010.01091.x
   Web of Science ®Google Scholar
• Kaushik, M., Trinkle, M., & Hashemi-Sakhtsari, A. (2010). Automatic detection and removal of disfluencies from spontaneous speech. Proceedings of the 13th Australasian International Conference on Speech Science and Technology, (pp. 98–101).
   Google Scholar
• Krikke, T. F., & Truong, K. P. (2013). Detection of nonverbal vocalizations using gaussian mixture models: Looking for fillers and laughter in conversational speech. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 163–167). International Speech and Communication Association.
   Google Scholar
• Kuhn, M. (2019). caret: classification and regression training. R package version 6.0-84. https://CRAN.R-project.org/package=caret.
   Google Scholar
• Ladefoged, P., & Johnson, K. (2011). A course in phonetics. Wadsworth.
   Google Scholar
• Levelt, W. J. M., Roelofs, A., & Meyer, A. S. (1999). A theory of lexical access in speech production. Behavioral and Brain Sciences, 22(1), 1–37. https://doi.org/https://doi.org/10.1017/s0140525x99001776
   Web of Science ®Google Scholar
• Moussalli, S., & Cardoso, W. (2020). Intelligent personal assistants: Can they understand and be understood by accented L2 learners? Computer Assisted Language Learning, 33(8), 865–890. https://doi.org/https://doi.org/10.1080/09588221.2019.1595664
   Web of Science ®Google Scholar
• O’Shaughnessy, D. (1992). Recognition of hesitations in spontaneous speech. Proceedings ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing (521-524). IEEE. https://doi.org/https://doi.org/10.1109/ICASSP.1992.225857
   Google Scholar
• Orr, R., & Quené, H. (2017). D-LUCEA: Curation of the UCU accent project data. In J. Odijk & A. van Hessen (Eds.), CLARIN in the low countries (pp. 177–190). Ubiquity Press.
   Google Scholar
• Reetz, H., & Jongman, A. (2009). Phonetics: Transcription, production, acoustics, and perception. Wiley-Blackwell.
   Google Scholar
• Révész, A., Ekiert, M., & Torgersen, E. N. (2016). The effects of complexity, accuracy, and fluency on communicative adequacy in oral task performance. Applied Linguistics, 37(6), 828–848. https://doi.org/https://doi.org/10.1093/applin/amu069
   Web of Science ®Google Scholar
• Rietveld, T., & Van Heuven, V. J. (2009). Algemene Fonetiek (third edition) [General phonetics]. Coutinho.
   Google Scholar
• Segalowitz, N. (2010). Cognitive bases of second language fluency. Routledge.
   Google Scholar
• Shriberg, E. (2001). To ‘errrr’ is human: Ecology and acoustics of speech disfluencies. Journal of the International Phonetic Association, 31(1), 153–169. https://doi.org/https://doi.org/10.1017/S0025100301001128
   Google Scholar
• Shriberg, E. E., & Lickley, R. J. (1993). Intonation of clause-internal filled pauses. Phonetica, 50(3), 172–179. https://doi.org/https://doi.org/10.1159/000261937
   PubMed Web of Science ®Google Scholar
• Stouten, F., Duchateau, J., Martens, J. P., & Wambacq, P. (2006). Coping with disfluencies in spontaneous speech recognition: Acoustic detection and linguistic context manipulation. Speech Communication, 48(11), 1590–1606. https://doi.org/https://doi.org/10.1016/j.specom.2006.04.004
   Web of Science ®Google Scholar
• Stouten, F., & Martens, J. P. (2003). A feature-based filled pause detection system for Dutch. In 2003 IEEE Workshop on Automatic Speech Recognition and Understanding, (pp. 309–314. IEEE.
   Google Scholar
• Tavakoli, P., Nakatsuhara, F., & Hunter, A. M. (2017). Scoring validity of the aptis speaking test: Investigating fluency across tasks and levels of proficiency. ARAGs Research Reports Online. 2057-5203. https://www.britishcouncil.org/sites/default/files/tavakoli_et_al_layout.pdf
   Google Scholar
• Thiele, C., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, C., Mayer, Z., Kenkel, B., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., & Hunt, T. (2019). cutpointr: Determine and evaluate optimal cutpoints in binary classification tasks. R package version 0.7.6. https://CRAN.R-project.org/package=cutpointr.
   Google Scholar
• Vasilescu, I., & Adda-Decker, M. (2007). A cross-language study of acoustic and prosodic characteristics of vocalic hesitations. In A. Esposito, M. Bratanić, E. Keller, & M. Marinaro (Eds.), Fundamentals of verbal and nonverbal communication and the biometric issue (pp. 140–148). IOS Press.
   Google Scholar
• Verkhodanova, V., & Shapranov, V. (2016). Experiments on detection of voiced hesitations in Russian spontaneous speech. Journal of Electrical and Computer Engineering, 2016(8), Article ID 2013658. https://doi.org/https://doi.org/10.1155/2016/2013658
   Google Scholar