On sensitivity of vocal tract area functions

Nasal resonance is utilized in certain languages to differentiate word meanings. The joint filtering effect by the vocal tract and the nasal tract can be modeled by the auto-regression moving-average (ARMA) approach. However, unlike all-pole (i.e., AR) modeling, it has been difficult to derive the equivalent vocal-tract area function directly from an ARMA model due to the nonlinear nature in the relation between model coefficients and vocal-tract geometry. In this paper, we propose a method to decompose an ARMA model approximately into α/C(z) + β/D(z); in our context, 1/C(z) and 1/D(z) represent the filtering effects of the oral and the nasal tract, respectively. Once the decomposition is performed, equivalent oral-tract and nasal-tract area functions can be obtained by converting C(z) and D(z) to their respective lattice representation. The proposed method was applied to non-nasalized and nasalized vowels produced by three speakers, and it was found that the ratio r = β/α tends to be higher in nasalized vowels than in their non-nasalized counterparts. The vocal-tract area function estimated by the present approach was also fairly stable for sustained vowels.

This paper describes a method for estimating wind field (wind velocity, rate of change of wind velocity, and wind gradient) for small and mini unmanned aerial vehicles. The approach uses sensors that are already part of a standard autopilot sensor suite (Global Positioning System, inertial measurement unit, airspeed, and magnetometer). The primary motivation is enabling energy harvesting; a secondary motivation is development of a low-cost atmospheric measurement and sampling system. The paper presents an error analysis and discusses the primary contributions to error in the estimated wind field. Results of Monte Carlo simulations compare predicted errors in wind estimates with actual errors and show the effect of using estimated winds for energy harvesting from gusts.

Numerous studies have obtained a vocal tract area function using data obtained from magnetic resonance imaging (MRI). However, this approach often results in increased error between formant frequencies derived by recorded voice and estimated frequencies derived by MRI. Two factors engender error: creating the vocal tract model and estimating the area function from the vocal tract model. Generally, the errors from those two factors are not considered independently. This study is intended to establish precise estimation of the area function automatically from a vocal tract model. Pressure contours and sound intensity in the vocal tract are obtained using the finite-element method (FEM); the vocal tract area function is obtained from them. The precision is assessed by comparing the formant frequencies that are derived using the FEM with our method. Results show that the vocal tract area function can be estimated precisely from three-dimensional MRI.

Formant tracking linear prediction model using HMMs and Kalman filters for noisy speech processing

Acoustic effects of the time-varying glottal area due to vocal fold vibration on the laryngeal cavity resonance were investigated based on vocal tract area functions and acoustic analysis. The laryngeal cavity consists of the vestibular and ventricular parts of the larynx, and gives rise to a regional acoustic resonance within the vocal tract, with this resonance imparting an extra formant to the vocal tract resonance pattern. Vocal tract transfer functions of the five Japanese vowels uttered by three male subjects were calculated under open- and closed-glottis conditions. The results revealed that the resonance appears at the frequency region from 3.0 to 3.7 kHz when the glottis is closed and disappears when it is open. Real spectra estimated from open- and closed-glottis periods of vowel sounds also showed the on-off pattern of the resonance within a pitch period. Furthermore, a time-domain acoustic analysis of vowels indicated that the resonance component could be observed as a pitch-synchronized rise-and-fall pattern of the bandpass amplitude. The cyclic nature of the resonance can be explained as the laryngeal cavity acting as a closed tube that generates the resonance during a closed-glottis period, but damps the resonance off during an open-glottis period.

Advanced driver assistance systems have been an active research topic for decades, for which many approaches have been developed not only to reduce the number of traffic accidents but also to increase the driver’s comfort. Among the many different solutions proposed, learning-based prediction approaches have gained considerable attention in recent years. Within this scope, this work focuses on the implementation aspects of a linear learning-based regression model for detecting unintended lane-departures, where the goal is to achieve a prediction model with good predictive performance while keeping the computational complexity as low as possible. Aspects under consideration include input signal selection and down-sampling. The linear prediction model is analyzed using a real world data set, and benchmarked against a kinematic constant velocity model and a non-linear regression model. The results show that the linear regression model has a significantly higher prediction performance when compared to a kinematic model. It is also shown that the predictive performance remains comparable to the more complex nonlinear regression model, even though the computational complexity of the linear model is significantly lower.

A mathematical model for generating vocal tract area functions for vowel‐like sounds has been developed. Using computer simulation of a transmission line analog of the vocal tract, the relationship between acoustic features (in this case formants) and parameters of the model (i.e., articulatory parameters) has been investigated in order to develop improved algorithms for acoustic‐articulatory and articulatory‐acoustic transformations. The model has been found to be a useful tool for study of acoustic phonetics, examination of the applicability of speaker normalization techniques, and for the refinement of high‐quality speech synthesis. Extensions of the model to include consonantal vocal tract configurations are also presented.

The acoustic effects of the laryngeal cavity on the vocal tract resonance were investigated by using vocal tract area functions for the five Japanese vowels obtained from an adult male speaker. Transfer functions were examined with the laryngeal cavity eliminated from the whole vocal tract, volume velocity distribution patterns were calculated, and susceptance matching analysis was performed between the laryngeal cavity and the vocal tract excluding the laryngeal cavity (vocal tract proper). It was revealed that the laryngeal cavity generates one of the formants of the vocal tract, which is the fourth in the present study. At this formant, the resonance of the laryngeal cavity (the 1/4 wavelength resonance) induces the open-tube resonance of the vocal tract proper (the 3/2 wavelength resonance). At the other formants, on the other hand, the vocal tract proper acts as a closed tube, because the laryngeal cavity has only a small contribution to generating these formants and the effective closed end of the whole vocal tract is the junction between the laryngeal cavity and the vocal tract proper.

Development of a linear predictive model for carbon dioxide sequestration in deep saline carbonate aquifers

Both Chinese and foreign researchers have developed linear models to predict track irregularities. These models adopt the Track Quality Index (TQI) as an index to evaluate overall track irregularity. In these models, an averaging method is employed to calculate the slope k of track irregularity over a particular track section and k is then used to predict future TQIs. However, complicated factors affect track irregularity. Roadbed state, passing tonnage, train running speed and track structure are all important factors that determine track irregularity changes. Under the joint action of various factors, changes in track irregularity are characterized by nonlinearity, which can be described the so-called Bathtub Curve. Linear prediction models provide a simple but convenient tool to predict the evolution of track irregularities. In a certain sense, these models help to deepen research and can shed new light on the pattern of track irregularity changes. However, these models inevitably have two obvious disadvantages. The first is that no systematic consideration is given to the inherent characteristics of track irregularity changes, while the second is that it is apparently impractical to use a single slope k to predict track irregularities for all track sections of a whole railway line. The result is that when linear models are used to predict track irregularity changes, big predictive errors are caused. In the light of the cyclic, exponential and multistage changes in track irregularity, the current authors have developed a multistage linear prediction model for track irregularity. Given the cyclic nature of track irregularity changes, we take a 200 m track section as the unit section for our research. Because track irregularities change at different rates within one track irregularity development cycle, we divide such changes into several shorter stages. Based on our understanding of the three major characteristics of track irregularity changes, we put forward an improved linear prediction model, namely the multistage linear prediction model (MLPM). The model adjusts the calculation method of k to make the predicted results fit better with the actually measured values. The authors finally use track car inspection data from the Beijing-Shanghai Railway Line (Jing-Hu Line) to validate the model and compare the predicted results with the measured data.

A framework for adaptive bandwidth management in ATM based networks is presented. The central concept of this approach is an adaptive estimation of the aggregate effective bandwidth required by connections carried on each link of the network. To achieve reliable results the estimation process takes into account both the traffic source declarations and the connection superposition process measurements on the network links. This is done in an optimization framework provided by estimation theory. In the paper the authors concentrate on evaluation of a bandwidth reserved for possible estimation error. For this purpose they use the error covariance matrix from a linear two state Kalman filter applied for the system state estimation. The bandwidth reserved for the estimation error provides that the source parameter declarations can be more relaxed and that the source policing can be less stringent.

We uncover the reality of traffic differentiation in mobile virtual network operator (MVNO) networks and shed new light on its negative impact on the accuracy of available bandwidth (i.e., the unused capacity of an end-to-end path) estimation methods that use a probing packet train (i.e., a set of multiple probing packets). To the best of our knowledge, we report, for the first time, that multiple operational LTE MVNO networks in the Asian region are very likely to differentiate traffic according to the time of day. We find, also for the first time, that when a sender transmits a packet train to a receiver during a period of traffic differentiation, the packet loss rate of the packet train jumps up, thus degrading the accuracy of available bandwidth estimation as the packet train is corrupted by the high packet loss rate. As a countermeasure, we propose a method called PathQuick4 for accurately estimating the available bandwidth while remaining robust against traffic differentiation. The key idea is that we dynamically judge whether traffic differentiation occurs through inline measurement of packet loss characteristics by using a single packet train itself, without transmitting any extra probing packets. From this judgement, we then choose either packet-train-or packet-pair-based bandwidth estimation as appropriate. An experimental evaluation over three operational Japanese LTE MVNO networks showed that the overall estimation error of PathQuick4 across the three MVNOs during periods of traffic differentiation was close to an order of magnitude less than the error of a conventional method.

Gait in the human body measured and expressed in a biomedical signal stored in the patient's medical record. Gait is a time-series signal, depends on time changes and require a large-enough storage capacity. In this study, an optimization method based on data reduction used to compress gait data using linear prediction modeling. The estimation signal from the method decomposed using discrete wavelet transform (DWT). The estimation signal used to maintain the authenticity of the information in the gait signal. Linear modeling with order value from 8 to 11 generated similar signal with error value up to 1.67 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . Daubechies wavelet used to decompose the signal with compression level up to 25.5259%. The results of the research show that the compressed signal has a simpler data size while maintaining the value of the original data. With a smaller capacity, the designed gait database will have more efficient storage space requirements.

A transformation is described which converts the vocal tract areas derived from the linear prediction model to vocal tract widths. The transformation is described by the equation w = c(kA)^{p} where w = vocal tract width A = vocal tract area c = constant p = power constant and k = absolute areas criterion. Optimum values of c are given and the best criterion for making the linear prediction areas absolute was that the maximum area is a constant. The transform has been implemented in a real-time articulatory display which gives an overhead of only 14% over simple linear prediction area extraction. The display was found to be capable of showing the position of any vowel. Potential applications include its use as an aid to articulator placement in deaf speech training.

Two methods are described for determining the vocal tract area function from measurements at the lips of the response to an impulsive acoustic pressure wave: - In one case, the impulse response at the lips obtained by deconvolution is used to compute the area function at all points; - Alternatively, the vocal tract is modeled by successive approximation, a search is made for the constrictions by decreasing order of importance rather than sequentially from the lips. Results obtained with both methods are good, the area functions are measured in absolute values rather than in arbitrary units. Advantages, difficulties and limitations of both methods are discussed.