Efforts in ambulatory methods are heading in this direction [17; 18; 19], but there are many associated challenges. Some of the ambulatory voice monitors use either a microphone signal to estimate fundamental frequency (\(f_{0}\)) and jitter [21], a surface electromyograph to estimate increased muscle tension [22], or a neck-surface accelerometer over the extrathoracic trachea to estimate sound pressure level, fundamental frequency, voicing activity, vocal dose, and related measures[15; 23; 24; 25; 26; 27; 28], as well as aerodynamic, cepstral and related parameters [14; 16]. Aerodynamic measures have been successfully used to differentiate both phonotraumatic and non-phonotraumatic VH patients from matched controls using sustained vowels [9; 29], and have been shown to become salient features of compensatory mechanisms associated with VH in modeling studies [30; 31]. Thus, these aerodynamic measures have a strong potential to enhance the ability to identify VH in ambulatory settings [16]. Given that traditional assessment of aerodynamic signals using a Rothenberg mask [32] is not feasible for ambulatory scenarios, indirect estimation methods are required. The Subglottal Impedance-Based Inverse Filtering (IBIF) scheme [33] allows for estimating the glottal airflow signal from neck-surface vibration. The IBIF approach was recently tested in a discrimination task using week-long ambulatory recordings for 50 patients with vocal fold nodules and 50 matched healthy-control subjects [16]. The results of classification task using aerodynamic features outperformed previous efforts with other measures [14; 15; 17; 19] and provides a new avenue to improve the assessment and treatment of VH disorders. Despite of these advances, unquantified uncertainties are associated to the estimation of the glottal airflow signal with the IBIF scheme due to a number of factors. First, the determination of the IBIF model parameters uses inverse filtering of the oral airflow from few sustained vowel samples, which can lead to IBIF parameter variations for different vowels and pitch conditions [16; 34]. The latter becomes especially challenging for high-pitched female voices, which are common in ambulatory studies. In addition, there are combined measurement uncertainties from the accelerometer due to sensor positioning, skin attachment, temperature, etc. Furthermore, there is no direct reference that can be used to quantify these combined effects in ambulatory scenarios. Thus, there is a need to quantify the magnitude of the uncertainty in the estimation process, and to potentially improve the estimation of the aerodynamic signals through the IBIF framework. To address the aforementioned limitations, we propose a Kalman filter (KF) implementation of the IBIF filter, which allows for both assessing the estimation uncertainty and correcting for potential deviations in the airflow signal estimates. The KF structure is based in a Moving Average (MA) Kalman Filter with colored state noise modeling the glottal airflow signal. To assess the accuracy of the KF, we compare aerodynamic measures describing the glottal airflow signal obtained from the oral mask using a notch-filter [35; 36], the standard IBIF [33], and the modified Kalman filter for a group of PVH and healthy-controls subjects reading a phonetically balanced passage. The paper is structured as follows: In Section 2, we present the methods utilized to estimate glottal airflow, namely the IBIF method and its Kalman filter implementation. Then, in Section 3, we describe the experimental setup with participants with PVH and vocally healthy control subjects. In Section 4, we present the results of the experiments, and in Section 5, we discuss them in detail. Finally, in Section 6, we present the conclusions and future work.