# Deep Learning for Neuromuscular Control of Vocal Source for Voice Production
###### Abstract
There is no internal recall schema for preferred paths through a maze of physical parameters. All explorations begin from scratch. Currently, there are a limited number of studies addressing the neural control of vocal source and vocal tract [11; 12; 13; 14]. In particular, the DIVA neural control model for the vocal tract was recently expanded to include the control of the body-cover vocal source model (named LaDIVA) [15]. The acoustic targets for LaDIVA are the fundamental frequency (f\({}_{\text{o}}\)) and sound pressure level (SPL), and the control parameters are subglottal pressure (P\({}_{\text{s}}\)), and cricothyroid (CT) and thyroarytenoid (TA) muscle activations. Somatosensory targets are not included in the current version of LaDIVA. The current study used a similar architecture to the DIVA model, i.e., one feedforward controller, one acoustic feedback controller, and one somatosensory feedback controller. However, the two differ in the composition of the controllers. The DIVA/LaDIVA model used linear Jacobian matrices to model the feedback controllers while the current study developed non-linear deep neural networks-based controllers, allowing more complex control mechanisms. Also, the current study included acoustic measures of voice quality (brightness and roughness) as acoustic targets, along with f\({}_{\text{o}}\) and SPL. Control parameters included lateral cricoarytenoid muscle activation along with the lung pressure (P\({}_{\text{L}}\)), CT and TA activations. In contrast to the LaDIVA model, four somatosensory targets (vocal fold length, and fiber stress in the three vocal fold layers) were also included. Machine learning and artificial intelligence are gaining traction in voice and speech research. They are being used for the automatic detection of voice disorders [16; 17; 18], improving the computational efficiency of simulators [19], and estimation of voice control parameters from acoustic output signals [20; 21]. Currently, machine learning is being used to detect disorders such as laryngeal cancer, dysphonia, vocal fold nodules, polyps, edema, vocal fold paralysis, and neuromuscular disorders from voice and speech samples. Additionally, computationally expensive components of the traditional voice and speech production models such as the flow and pressure calculations are being replaced by faster machine learning models [19]. Unsupervised learning techniques are being used for disorder detection [22], emotion recognition [23], and voice quality detection using voice and speech samples [24]. Reinforcement learning is another branch of machine learning where the training data are not needed ahead of time [25]. The model learns to control the physical plant by interacting with it in a trial-and-error manner. The reinforcement learning approach can be used to learn the control of the vocal system. These neural control systems try to mimic how the brain controls the vocal system. When used with voice simulators, such neural control systems can provide insights into neuromuscular disorders such as vocal tremor, Parkinson's disease, and spasmodic dysphonia. Even though the DIVA model, and other neural controllers of the vocal system, including the control system developed in the current study do not use reinforcement learning in its true sense, they fall under its broader category. The primary objective of the current study was to develop a deep learning-based neural control system for the vocal source with feedforward and feedback controllers that generates lung pressure and intrinsic laryngeal muscle activation levels to achieve acoustic targets of interest. The secondary objective was to understand the relative role of the feedback and feedforward controllers towards the total motor command.