Phonetic posteriorgram-based voice conversion system to improve speech intelligibility of dysarthric patients

End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

(2018) Szu-Wei Fu et al.   IEEE-ACM Transactions on Audio Speech and Language Processing

Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery

(2017) Szu-Wei Fu et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

(2016) Carlos E. Galván-Tejada et al.   Mobile Information Systems

Artificial neural networks as speech recognisers for dysarthric speech: Identifying the best-performing set of MFCC parameters and studying a speaker-independent approach

(2014) Seyed Reza Shahamiri et al.   ADVANCED ENGINEERING INFORMATICS

Predicting the intelligibility of vocoded and wideband Mandarin Chinese

(2011) Fei Chen et al.   JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA

An Acoustic Study of the Relationships Among Neurologic Disease, Dysarthria Type, and Severity of Dysarthria

(2010) Yunjung Kim et al.   JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH