Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Speech provides a potential simple and noninvasive “on-body” means to identify and monitor neurological diseases. Here  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 beten vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework,  test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vols. Using subsequent modulation features derived from the model,  predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components,  evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation beten the actual and predicted MDD scores.  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.
['Rachelle L. Horwitz-Martin', 'Thomas F. Quatieri', 'Elizabeth Godoy', 'James R. Williamson']
I am writing a research paper on a idea A vocal modulation model with application to predicting depression severity
[{"from": "human", "value": "I am writing a research paper on a idea A vocal modulation model with application to predicting depression severity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Speech provides a potential simple and noninvasive \u201con-body\u201d means to identify and monitor neurological diseases. Here  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 beten vocal tract resonances (formants) and frequency modulation in vocal fold harmonics. Based on the model framework,  test three methods to extract envelopes capturing these modulations of the third formant for synthesized sustained vols. Using subsequent modulation features derived from the model,  predict MDD severity scores with a Gaussian Mixture Model. Performing global optimization over classifier parameters and number of principal components,  evaluate performance of the features by examining the root-mean-squared error (RMSE), mean absolute error (MAE), and Spearman correlation beten the actual and predicted MDD scores.  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."}]