PhonoQ

PhonoQ is a deep learning model used to compute phonetic-based features related to duration, rate, rhythm*, and goodness of pronunciation* of 18 phonological classes

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Cite as

• Arias-Vergara, T. (2022). Analysis of Pathological Speech Signals. Logos Verlag Berlin GmbH,Vol. 50.

Related publications

• Arias-Vergara, T., Londoño-Mora, E., Pérez-Toro, P.A., Schuster, M., Nöth, E., Orozco-Arroyave, J.R., Maier, A. (2023) Measuring Phonological Precision in Children with Cleft Lip and Palate. Proceedings of the 24th INTERSPEECH, pp. 4638-4642.

• Pérez-Toro, P. A., Rodríguez-Salas, D., Arias-Vergara, T., Bayerl, S. P., Klumpp, P., Riedhammer, K., ... & Orozco-Arroyave, J. R. (2023, June). Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5.

• Pérez-Toro, P.A., Arias-Vergara, T., Braun, F., Hönig, F., Tobón-Quintero, C.A., Aguillón, D., Lopera, F., Hincapié-Henao, L., Schuster, M., Riedhammer, K., Maier, A., Nöth, E., Orozco-Arroyave, J.R. (2023) Automatic Assessment of Alzheimer's across Three Languages Using Speech and Language Features. Proceedings of the 24th INTERSPEECH, pp. 1748-1752.