Publications

Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS. Features were derived from statistics of the first (F1) and second (F2) formant frequency trajectories and their first and second derivatives. Motivated by a possible link between components of formant dynamics and specific articulator movements, these features were also computed for low-pass and high-pass filtered formant trajectories. When compared to clinician-rated intelligibility and speaking rate assessments, F2 features, particularly mean F2 speed and a novel feature, mean F2 acceleration, were most strongly correlated with intelligibility and speaking rate, respectively (Spearman correlations > 0.70, p < 0.0001). These features also yielded the best predictions in regression experiments (r > 0.60, p < 0.0001). Comparable results were achieved using low-pass filtered F2 trajectory features, with higher correlations and lower prediction errors achieved for speaking rate over intelligibility. These findings suggest information can be exploited in specific frequency components of formant trajectories, with implications for automatic monitoring of ALS.

Speech provides a potential simple and noninvasive "on-body" means to identify and monitor neurological diseases. Here we develop a model for a class of vocal biomarkers exploiting modulations in speech, focusing on Major Depressive Disorder (MDD) as an application area. Two model components contribute to the envelope of the speech waveform: amplitude modulation (AM) from respiratory muscles, and AM from interaction between vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework, we test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vowels. Using subsequent modulation features derived from the model, we predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components, we evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation between the actual and predicted MDD scores. We achieved RMSE and MAE values 10.32 and 8.46, respectively (Spearman correlation=0.487, p<0.001), relative to a baseline RMSE of 11.86 and MAE of 10.05, obtained by predicting the mean MDD severity score. Ultimately, our model provides a framework for detecting and monitoring vocal modulations that could also be applied to other neurological diseases.