Most electrolarynges do not allow hands-free use or pitch modulation. This study presents the potential of pattern recognition to support electrolarynx use by predicting fundamental frequency (F0) and voicing state (VS) from neck surface EMG and respiratory trace. Respiratory trace and neck surface EMG were collected from 10 normal, adult males (18-60 years old) during different vocal tasks. Time-domain features were extracted from both signals, and a Support Vector Machine (SVM) classifier was employed to model F0 and VS. An average mean-squared-error (MSE) of 8.21 ± 3.5 semitones2 was achieved for the estimation of vocal frequency. An average classification accuracy of 78.05 ± 6.3 % was achieved for the prediction of voicing state from EMG and 65.24 ± 7.8 % from respiratory trace. Our results show that pattern classification of neck-muscle EMG and respiratory trace has merit in the prediction of F0 and VS during vocalization.

Most electrolarynges do not allow hands-free use or pitch modulation. This study presents the potential of pattern recognition to support electrolarynx use by predicting fundamental frequency (F0) and voicing state (VS) from neck surface EMG and respiratory trace. Respiratory trace and neck surface EMG were collected from 10 normal, adult males (18-60 years old) during different vocal tasks. Time-domain features were extracted from both signals, and a Support Vector Machine (SVM) classifier was employed to model F0 and VS. An average mean-squared-error (MSE) of 8.21 ± 3.5 semitones2 was achieved for the estimation of vocal frequency. An average classification accuracy of 78.05 ± 6.3 % was achieved for the prediction of voicing state from EMG and 65.24 ± 7.8 % from respiratory trace. Our results show that pattern classification of neck-muscle EMG and respiratory trace has merit in the prediction of F0 and VS during vocalization.