Browsing by Department "Centro Avanzado de Ingeniería Eléctrica y Electrónica - AC3E"

Inverse filtering of oral airflow using closed-phase linear prediction is expected to preserve the effects of source-filter interactions in the glottal airflow pulse. Under incomplete glottal closure, the glottal airflow estimation is more challenging due to a lowered glottal impedance, increased subglottal coupling, and violated all-pole assumption. To account for these effects, a model-based inverse filtering scheme allowing for coupling between glottis and upper and lower airways was developed. Acoustic transmission in the tracts used a frequency-domain transmission line. A linearized, time-varying expression was used for the glottal impedance, along with a dipole representation. Synthetic vowels sounds and actual recordings were used to evaluate the proposed scheme. Subject-specific model parameters were obtained from simultaneous aerodynamic, acoustic, and high-speed videoendoscopic recordings of normal subjects uttering vowels with various degrees of glottal closure. Results illustrated that, even under incomplete glottal closure, the airflow entering the vocal tract preserved source-filter interactions and was comparable to that obtained using closed-phase linear prediction. The scheme also yielded an uncoupled glottal airflow that exhibited a clear pulse de-skewing, making it proportional to the glottal area. Cases with larger glottal gaps exhibited lower mean impedances and less pulse skewing, with airflow estimates proportional to the transglottal pressure drop.

Previous vocal fold modeling studies have generally focused on generating detailed data regarding a narrow subset of possible model configurations. These studies can be interpreted to be the investigation of a single subject under one or more vocal conditions. In this study, a broad population-based sensitivity analysis is employed to examine the behavior of a virtual population of subjects and to identify trends between virtual individuals as opposed to investigating a single subject or model instance. Four different sensitivity analysis techniques were used in accomplishing this task. Influential relationships between model input parameters and model outputs were identified, and an exploration of the model's parameter space was conducted. Results indicate that the behavior of the selected two-mass model is largely dominated by complex interactions, and that few input-output pairs have a consistent effect on the model. Results from the analysis can be used to increase the efficiency of optimization routines of reduced-order models used to investigate voice abnormalities. Results also demonstrate the types of challenges and difficulties to be expected when applying sensitivity analyses to more complex vocal fold models. Such challenges are discussed and recommendations are made for future studies.

The development of trauma-induced lesions of the vocal folds (VFs) has been linked to a high collision pressure on the VF surface. However, there are no direct methods for the clinical assessment of VF collision, thus limiting the objective assessment of these disorders. In this study, we develop a video processing technique to directly quantify the mechanical impact of the VFs using solely laryngeal kinematic data. The technique is based on an edge tracking framework that estimates the kinematic sequence of each VF edge with a Kalman filter approach and a Hertzian impact model to predict the contact force during the collision. The proposed formulation overcomes several limitations of prior efforts since it uses a more relevant VF contact geometry, it does not require calibrated physical dimensions, it is normalized by the tissue properties, and it applies a correction factor for using a superior view only. The proposed approach is validated against numerical models, silicone vocal fold models, and prior studies. A case study with high-speed videoendoscopy recordings provides initial insights between the sound pressure level and contact pressure. Thus, the proposed method has a high potential in clinical practice and could also be adapted to operate with laryngeal stroboscopic systems.

The amplitude of auditory steady-state responses (ASSRs) generated in the brainstem of rats exponentially decreases over the sequential averaging of EEG epochs. This behavior is partially due to the adaptation of the ASSR induced by the continuous and monotonous stimulation. In this study, we analyzed the potential clinical relevance of the ASSR adaptation. ASSR were elicited in eight anesthetized adult rats by 8-kHz tones, modulated in amplitude at 115 Hz. We called independent epochs to those EEG epochs acquired with sufficiently long inter-stimulus interval, so the ASSR contained in any given epoch is not affected by the previous stimulation. We tested whether the detection of ASSRs is improved when the response is computed by averaging independent EEG epochs, containing only unadapted auditory responses. The improvements in the ASSR detection obtained with standard, weighted and sorted averaging were compared. In the absence of artifacts, when the ASSR was elicited by continuous acoustic stimulation, the computation of the ASSR amplitude relied upon the averaging method. While the adaptive behavior of the ASSR was still evident after the weighting of epochs, the sorted averaging resulted in under-estimations of the ASSR amplitude. In the absence of artifacts, the ASSR amplitudes computed by averaging independent epochs did not depend on the averaging procedure. Averaging independent epochs resulted in higher ASSR amplitudes and halved the number of EEG epochs needed to be acquired to achieve the maximum detection rate of the ASSR. Acquisition protocols based on averaging independent EEG epochs, in combination with appropriate averaging methods for artifact reduction might contribute to develop more accurate hearing assessments based on ASSRs.

Modeling the vocal fold structure as a reduced-order system is an attractive approach for exploring the dynamics of both normal and pathological phonation. This approach has been used ubiquitously in scientific speech investigations due to its relatively high order of accuracy and low computational cost. In addition, good agreement can also be found between model and clinical data. In the case of pathological speech complex vocal fold dynamics may exist, exhibiting phenomenon such as bifurcation, and chaos. The ability to capture these features in reduced-order vocal fold models is a much celebrated feature. However, the question has arisen whether these nonlinearities arise due to the physics, or if they are merely an artifact of the model and its sensitivity to initial and boundary conditions. We explore the sensitivity of commonly-employed reduced-order vocal fold models to both contact mechanics, and the geometric prescription of the vocal fold model. Nonlinearities arising from asymmetric vocal fold tensioning are investigated. Nonlinearity in the vocal fold dynamics is identified by determining the predictive capability of linear and nonlinear Volterra-Weiner-Korenberg series. Nonlinearities in the vocal fold oscillations are shown to be highly-dependent upon model formulation and implementation, as opposed to physical features of speech.

The development of ambulatory voice monitoring devices has the potential to improve the diagnosis and treatment of voice disorders. In this proof-of-concept study, real-time biofeedback is incorporated into a smartphone-based platform that records and processes neck surface acceleration. The focus is on utilizing aerodynamic measures of vocal function as a basis for biofeedback. This is done using regressed Z-scores to compare recorded values to normative estimates based on sound pressure level and fundamental frequency. Initial results from the analysis of different voice qualities suggest that accelerometer-based estimates of aerodynamic parameters can be used for real-time ambulatory biofeedback.

There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required.

Miniature high-bandwidth accelerometers on the anterior neck surface are used in laboratory and ambulatory settings to obtain vocal function measures. This study compared the widely applied L1–L2 measure (historically, H1–H2)—the difference between the log-magnitude of the first and second harmonics—computed from the glottal airflow waveform with L1–L2 derived from the raw neck-surface acceleration signal in 79 vocally healthy female speakers. Results showed a significant correlation (r = 0.72) between L1–L2 values estimated from both airflow and accelerometer signals, suggesting that raw accelerometer-based estimates of L1–L2 may be interpreted as reflecting glottal physiological parameters and voice quality attributes during phonation.

Block-type lumped-element models of the vocal folds are widely used for speech investigations due in part to the rich dynamics exhibited over a range of input parameters, particularly for asymmetric tissue properties. While self-consistent in derivation and application, block-type models of the vocal fold masses are inherently susceptible to non-physical aerodynamic loading conditions when vocal fold motion is highly asymmetric. A standard block-type model is compared against two modified models that disallow the non-physiological loading condition. These minor modifications toward a more physiologically relevant aerodynamic model alter the specific vibration regimes and prevalence of chaos, though bifurcations still exist.

A critical element in understanding voice production mechanisms is the characterization of vocal fold collision, which is widely considered a primary etiological factor in the development of common phonotraumatic lesions such as nodules and polyps. This paper describes the development of a transoral, dual-sensor intraglottal/subglottal pressure probe for the simultaneous measurement of vocal fold collision and subglottal pressures during phonation using two miniature sensors positioned 7.6 mm apart at the distal end of a rigid cannula. Proof-of-concept testing was performed using excised whole-mount and hemilarynx human tissue aerodynamically driven into self-sustained oscillation, with systematic variation of the superior–inferior positioning of the vocal fold collision sensor. In the hemilarynx experiment, signals from the pressure sensors were synchronized with an acoustic microphone, a tracheal-surface accelerometer, and two high-speed video cameras recording at 4000 frames per second for top–down and en face imaging of the superior and medial vocal fold surfaces, respectively. As expected, the intraglottal pressure signal exhibited an impulse-like peak when vocal fold contact occurred, followed by a broader peak associated with intraglottal pressure build-up during the de-contacting phase. As subglottal pressure was increased, the peak amplitude of the collision pressure increased and typically reached a value below that of the average subglottal pressure. Results provide important baseline vocal fold collision pressure data with which computational models of voice production can be developed and in vivo measurements can be referenced.

Excessive vocal fold collision pressures during phonation are considered to play a primary role in the formation of benign vocal fold lesions, such as nodules. The ability to accurately and reliably acquire intraglottal pressure has the potential to provide unique insights into the pathophysiology of phonotrauma. Difficulties arise, however, in directly measuring vocal fold contact pressures due to physical intrusion from the sensor that may disrupt the contact mechanics, as well as difficulty in determining probe/sensor position relative to the contact location. These issues are quantified and addressed through the implementation of a novel approach for identifying the timing and location of vocal fold contact, and measuring intraglottal and vocal fold contact pressures via a pressure probe embedded in the wall of a hemi-laryngeal flow facility. The accuracy and sensitivity of the pressure measurements are validated against ground truth values. Application to in vivo approaches are assessed by acquiring intraglottal and VF contact pressures using a synthetic, self-oscillating vocal fold model in a hemi-laryngeal configuration, where the sensitivity of the measured intraglottal and vocal fold contact pressure relative to the sensor position is explored.

Many common voice disorders are chronic or recurring conditions that are likely to result from inefficient and/or abusive patterns of vocal behavior, referred to as vocal hyperfunction. The clinical management of hyperfunctional voice disorders would be greatly enhanced by the ability to monitor and quantify detrimental vocal behaviors during an individual’s activities of daily life. This paper provides an update on ongoing work that uses a miniature accelerometer on the neck surface below the larynx to collect a large set of ambulatory data on patients with hyperfunctional voice disorders (before and after treatment) and matched-control subjects. Three types of analysis approaches are being employed in an effort to identify the best set of measures for differentiating among hyperfunctional and normal patterns of vocal behavior: (1) ambulatory measures of voice use that include vocal dose and voice quality correlates, (2) aerodynamic measures based on glottal airflow estimates extracted from the accelerometer signal using subject-specific vocal system models, and (3) classification based on machine learning and pattern recognition approaches that have been used successfully in analyzing long-term recordings of other physiological signals. Preliminary results demonstrate the potential for ambulatory voice monitoring to improve the diagnosis and treatment of common hyperfunctional voice disorders.