Glottal Flow Waveform Research Articles

Comparing direct and indirect glottal flow waveform measurements in excised canine larynges and synthetic vocal fold models

Despite being considered the gold standard technique for delineating the flow glottogram during phonation, the inverse filtering technique has never been validated against actual volume flow measurements. This signal-processingtechnique uses the speech signal with vocal tract estimates to calculate the glottal flow waveform. The lack of validation stems from the difficulty of obtaining in-vivo flow measurements at the glottal exit. The current study aims to assess the accuracy of the inverse filtering technique by comparing its flow glottograms with direct, simultaneous volume flow measurement. A vocal tract with varied constrictions that mimic its shape according to specific vowels was connected to either an excised canine larynx or a synthetic vocal fold model. Direct volume flow measurements at the glottal exit were done using tomographic particle image velocimetry. Indirect measurements were obtained by fitting the known Rothenberg mask to the vocal tract and estimating the glottal flow waveform using the Glottal Enterprise system. Preliminary results indicate that the accuracy of the inverse filtering technique relies on the degree of vocal tract constriction. The flow glottogram aligns well with front, lax, and unrounded lip vowels, but its accuracy diminishes with tense and round vowels, particularly noticeable with the excised canine larynx.

The influence of source-filter interaction on the voice source in a three-dimensional computational model of voice production.

The goal of this computational study is to quantify global effects of vocal tract constriction at various locations (false vocal folds, aryepiglottic folds, pharynx, oral cavity, and lips) on the voice source across a large range of vocal fold conditions. The results showed that while inclusion of a uniform vocal tract had notable effects on the voice source, further constricting the vocal tract only had small effects except for conditions of extreme constriction, at which constrictions at any location along the vocal tract decreased the mean and peak-to-peak amplitude of the glottal flow waveform. Although narrowing in the epilarynx increased the normalized maximum flow declination rate, vocal tract constriction in general slightly reduced the source strength and high-frequency harmonic production at the glottis, except for a limited set of vocal fold conditions (e.g., soft, long vocal folds subject to relatively high pressure). This suggests that simultaneous laryngeal and vocal tract adjustments are required to maximize source-filter interaction. While vocal tract adjustments are often assumed to improve voice production, our results indicate that such improvements are mainly due to changes in vocal tract acoustic response rather than improved voice production at the glottis.

A comparative analysis of intraglottal geometry and velocity flow fields: Excised human, canine, and synthetic vocal fold models

Synthetic vocal fold models imitate the characteristics of human vocal folds and offer advantages over tissue models, such as accessibility and prolonged lifespan, all while producing comparable vibration frequencies to those in humans. Nonetheless, due to their simplified design, they may not fully capture the intricate behavior observed in tissue models like the canine larynx model. Canine larynges, like human’s, exhibit a vertical mucosal wave, which yields a phase delay between the inferior and superior edges of the vocal folds free margin. Consequently, a divergent glottis forms during closing, producing intraglottal flow separation vortices (FSVs) that increase voice loudness and intelligibility. To bridge the knowledge gap between silicone and tissue vocal fold models, we compared the intraglottal geometry and velocity flow fields during phonation of these three models. At low subglottal pressures, the synthetic model displayed small divergence angles and no flow separation, where both tissues models exhibited FSVs. At higher pressures, FSVs observed in tissue models were correlated with increased glottal flow waveform skewing and higher MFDR values. These findings highlight why the excised canine larynx model should be the preferred choice over synthetic vocal fold models for studying the aerodynamics and fluid-structure interaction during phonation.

The effect of vocal tract semi-occlusion on the voice source and implications for voice therapy

Semi-occluded vocal tract exercises are often used in voice therapy to improve voice production. It is generally assumed that by enhancing source-filter interaction, such vocal exercises improve vocal efficiency while minimizing vocal fold collision. The goal of this computational study is to quantify such effects of source–filter interaction on the voice source. Voice simulations were performed with vocal tract constriction at various locations (false vocal folds, aryepiglottic folds, pharynx, oral cavity, and lips). The results showed that effects of vocal tract constriction on the voice source were generally small except for conditions of extreme constriction, at which constrictions at any location along the vocal tract decreased the mean and peak-to-peak amplitude of the glottal flow waveform. In general, source-filter interaction that reduced vocal fold contact pressure also decreased sound pressure level, thus making it impractical when targeting a desired output vocal intensity. While source–filter interaction is often assumed to improve vocal efficiency and economy, our results indicate that such improvements are mainly due to improvement in the filter rather than the voice source at the glottis. It is possible that speakers make additional laryngeal adjustments during vocal exercises, which may also contribute to the therapeutic benefits of such exercises.

MGlif: A software tool for manual glottal inverse filtering

Despite decades of advances in automated estimation of the voice source (glottal flow) from the speech signal, manual glottal inverse filtering remains the gold standard; yet, options are limited regarding available software tools. Here, a new software application for glottal-flow estimation is introduced, with a graphical user interface designed for manual specification and tuning of inverse-filter parameters while results are updated and displayed in real-time. The user can flexibly control the signal polarity and sampling rate, the analysis frame position and duration, and the type of window and preemphasis for vocal-tract modeling. The tunable inverse-filter parameters are the frequencies and bandwidths of vocal-tract poles and zeros, and the lip-radiation coefficient. A single window displays graphical information in several panels: the speech waveform (scrollable), the selected frame, its log-power spectrum before and after preemphasis, the model spectrum including vocal-tract and lip-radiation effects, the estimated glottal-flow waveform, its derivative, and their log-power spectra. All information pertaining to an analysis may be saved at any time, ready to be reloaded for review or analysis resumption. Key data may also be exported for further use outside the application. The software will be made freely available to the research community.

Optical Flow Glottovibrogram for the examination of vocal fold pathology.

Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance- Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.

Deriving Vocal Fold Oscillation Information from Recorded Voice Signals Using Models of Phonation.

During phonation, the vocal folds exhibit a self-sustained oscillatory motion, which is influenced by the physical properties of the speaker's vocal folds and driven by the balance of bio-mechanical and aerodynamic forces across the glottis. Subtle changes in the speaker's physical state can affect voice production and alter these oscillatory patterns. Measuring these can be valuable in developing computational tools that analyze voice to infer the speaker's state. Traditionally, vocal fold oscillations (VFOs) are measured directly using physical devices in clinical settings. In this paper, we propose a novel analysis-by-synthesis approach that allows us to infer the VFOs directly from recorded speech signals on an individualized, speaker-by-speaker basis. The approach, called the ADLES-VFT algorithm, is proposed in the context of a joint model that combines a phonation model (with a glottal flow waveform as the output) and a vocal tract acoustic wave propagation model such that the output of the joint model is an estimated waveform. The ADLES-VFT algorithm is a forward-backward algorithm which minimizes the error between the recorded waveform and the output of this joint model to estimate its parameters. Once estimated, these parameter values are used in conjunction with a phonation model to obtain its solutions. Since the parameters correlate with the physical properties of the vocal folds of the speaker, model solutions obtained using them represent the individualized VFOs for each speaker. The approach is flexible and can be applied to various phonation models. In addition to presenting the methodology, we show how the VFOs can be quantified from a dynamical systems perspective for classification purposes. Mathematical derivations are provided in an appendix for better readability.

Evaluation of glottal inverse filtering in the presence of source-filter interaction

Glottal inverse filtering is often used to recover the glottal flow waveform from the produced voice outcomes. While there have been many studies aiming to evaluate its validity, few studies investigated its validity in voice conditions with strong source-filter interaction. The goal of this study is to evaluate the performance of glottal inverse filtering in conditions of epilaryngeal tube narrowing and/or reduced lip opening, both of which are expected to enhance source-filter interaction. Computational simulation of voice production was performed using a three-dimensional vocal fold model coupled to a vocal tract with varying degrees of constriction at the epilarynx and lips. Different algorithms of inverse filtering were then used to estimate the glottal airflow waveform from either the produced voice or airflow at the lips, and compared to the true glottal airflow from the simulation. The estimation errors in the glottal flow waveform and selected glottal flow-based measures will be evaluated as a function of the epilaryngeal and lip openings.

Fluid-Structure Interaction Analysis of Aerodynamic and Elasticity Forces During Vocal Fold Vibration

The effect of the intraglottal vortices on the glottal flow waveform was explored using flow-structure-interaction (FSI) modeling. These vortices form near the superior aspect of the vocal folds during the closing phase of the folds' vibration. The geometry of the vocal fold was based on the well-known M5 model. The model did not include a vocal tract to remove its inertance effect on the glottal flow. Material properties for the cover and body layers of the folds were set using curve fit to experimental data of tissue elasticity. A commercially available FSI solver was used to perform simulations at low and high values of subglottal input pressure. Validation of the FSI results showed a good agreement for the glottal flow and the vocal fold displacement data with measurements taken in the excised canine larynx model. The simulations result further support the hypothesis that intraglottal vortices can affect the glottal flow waveform, specifically its maximum flow declination rate (MFDR). It showed that MFDR occurs at the same phase when the highest intraglottal vortical strength and the negative pressure occur. It also showed that when MFDR occurs, the magnitude of the aerodynamic force acting on the glottal wall is greater than the elastic recoil force predicted in the tissue. These findings are significant because nearly all theoretical and computational models that study the vocal fold vibrations mechanism do not consider the intraglottal negative pressure caused by the vortices as an additional closing force acting on the folds.

Comparison of waveform estimated using inverse filtering with direct measurement of the volume flow at the glottal exit

Inverse filtering is a signal processing technique used to estimate the glottal waveform from the speech signal. Although this technique is regularly used in research studies, it has never been formally validated because of the difficulties in obtaining direct measurements of the glottal airflow. The objective of this study is a first step towards validating this technique by comparing its estimated glottal waveform with flow measurements taken simultaneously at the glottal exit. The setup is based on synthetic vocal folds connected to a vocal tract model. Direct measurements of the volume flow at the glottal exit are taken using time-resolved tomographic particle image velocimetry. A circumferentially vented pneumotachograph (i.e., Rothenberg) mask is connected to the vocal tract and used with the Glottal Enterprise system to calculate the glottal waveform using inverse filtering. Effect of varying subglottal pressures and effect of near-field (i.e., the minimal gap between the false vocal folds) and far-field (i.e., oral opening) constrictions were also investigated. Results show that overall using inverse filtering gives a good approximation of the glottal flow waveform, but its accuracy can change depending on the constrictions in the vocal tract. The clinical implication of these findings will be further discussed.

Computational Modeling of Voice Production Using Excised Canine Larynx.

A combined experimental-numerical work was conducted to comprehensively validate a subject-specific continuum model of voice production in larynx using excised canine laryngeal experiments. The computational model is a coupling of the Navier-Stokes equations for glottal flow dynamics and a finite element model of vocal fold dynamics. The numerical simulations employed a cover-body vocal fold structure with the geometry reconstructed from magnetic resonance imaging scans and the material properties determined through an optimization-based inverse process of experimental indentation measurement. The results showed that the simulations predicted key features of the dynamics observed in the experiments, including the skewing of the glottal flow waveform, mucosal wave propagation, continuous increase of the divergent angle and intraglottal swirl strength during glottal closing, and flow recirculation between glottal jet and vocal fold. The simulations also predicted the increase of the divergent angle, glottal jet speed, and intraglottal flow swirl strength with the subglottal pressure, same as in the experiments. Quantitatively, the simulations over-predicted the frequency and jet speed and under-predicted the flow rate and divergent angle for the larynx under study. The limitations of the model and their implications were discussed.

Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion

Estimation of physiological control parameters of the vocal system from the produced voice outcome has important applications in clinical management of voice disorders . Previously we developed a simulation-based neural network for estimation of vocal fold geometry, mechanical properties, and subglottal pressure from voice outcome features that characterize the acoustics of the produced voice. The goals of this study are to (1) explore the possibility of improving the estimation accuracy of physiological control parameters by including voice outcome features characterizing vocal fold vibration; and (2) identify voice feature sets that optimize both estimation accuracy and robustness to measurement noise. Feedforward neural networks are trained to solve the inversion problem of estimating the physiological control parameters of a three-dimensional body-cover vocal fold model from different sets of voice outcome features that characterize the simulated voice acoustics, glottal flow, and vocal fold vibration. A sensitivity analysis is then performed to evaluate the contribution of individual voice features to the overall performance of the neural networks in estimating the physiologic control parameters. While including voice outcome features characterizing vocal fold vibration increases estimation accuracy, it also reduces the network's robustness to measurement noise, due to high sensitivity of network performance to voice outcome features measuring the absolute amplitudes of the glottal flow and area waveforms, which are also difficult to measure accurately in practical applications. By excluding such glottal flow-based features and replacing glottal area-based features by their normalized counterparts, we are able to significantly improve both estimation accuracy and robustness to noise. We further show that similar estimation accuracy and robustness can be achieved with an even smaller set of voice outcome features by excluding features of small sensitivity.

Finite-element modeling of vocal fold self-oscillations in interaction with vocal tract: Comparison of incompressible and compressible flow model

Finite-element modeling of self-sustained vocal fold oscillations during voice production has mostly considered the air as incompressible, due to numerical complexity. This study overcomes this limitation and studies the influence of air compressibility on phonatory pressures, flow and vocal fold vibratory characteristics. A two-dimensional finite-element model is used, which incorporates layered vocal fold structure, vocal fold collisions, large deformations of the vocal fold tissue, morphing the fluid mesh according to the vocal fold motion by the arbitrary Lagrangian-Eulerian approach and vocal tract model of Czech vowel [i:] based on data from magnetic resonance images. Unsteady viscous compressible or incompressible airflow is described by the Navier-Stokes equations. An explicit coupling scheme with separated solvers for structure and fluid domain was used for modeling the fluid-structure-acoustic interaction. Results of the simulations show clear differences in the glottal flow and vocal fold vibration waveforms between the incompressible and compressible fluid flow. These results provide the evidence on the existence of the coupling between the vocal tract acoustics and the glottal flow (Level 1 interactions), as well as between the vocal tract acoustics and the vocal fold vibrations (Level 2 interactions).

Investigation and Evaluation of Glottal Flow Waveform for Voice Pathology Detection

Automatic voice pathology detection can provide objective estimation and prevention in the early stages of voice diseases. Glottal flow waveform directly reflects the state of glottal excitation. Extracting acoustic features from glottal source signals may contribute to the detection of pathological voice. To improve the performance of voice pathology detection, this article investigates the contribution of the glottal flow waveform for pathological voice detection by evaluating the classification result using features extracted from raw speech utterances and corresponding glottal flow waveforms. The individual feature sets used are extracted from raw or glottal voice utterances with identical parameter settings, which are openSMILE acoustic features, audio features computed by Moving Picture Experts Group-7 standard and classical glottal source features. In addition, a feature selection method in terms of the wrapper approach is used to combine the single features ranked by using the Fisher discrimination ratio. Voice pathology detection experiments were carried out using Random Forest. The best accuracies of 88.52% for the Saarbrucken Voice database and 100.00% for the Massachusetts Eye and Ear Infirmary database are achieved using the combined feature set extracted from the glottal source signal, with improvement of 0.44-3.13% in the accuracies obtained by using raw speech utterances. Compared to state-of-the-art methods, the proposed method achieves the highest accuracy for the Massachusetts Eye and Ear Infirmary database and an increase of 2.75-17.16% in detection accuracy compared to other conventional pipeline systems for the Saarbrucken Voice databse. The experimental results demonstrate that using glottal flow waveform as source signal can improve the performance of pathological voice detection.

Analysis of glottal inverse filtering in the presence of source-filter interaction

The validity of glottal inverse filtering (GIF) to obtain a glottal flow waveform from radiated pressure signal in the presence and absence of source-filter interaction was studied systematically. A driven vocal fold surface model of vocal fold vibration was used to generate source signals. A one-dimensional wave reflection algorithm was used to solve for acoustic pressures in the vocal tract. Several test signals were generated with and without source-filter interaction at various fundamental frequencies and vowels. Linear Predictive Coding (LPC), Quasi Closed Phase (QCP), and Quadratic Programming (QPR) based algorithms, along with supraglottal impulse response, were used to inverse filter the radiated pressure signals to obtain the glottal flow pulses. The accuracy of each algorithm was tested for its recovery of maximum flow declination rate (MFDR), peak glottal flow, open phase ripple factor, closed phase ripple factor, and mean squared error. The algorithms were also tested for their absolute relative errors of the Normalized Amplitude Quotient, the Quasi-Open Quotient, and the Harmonic Richness Factor. The results indicated that the mean squared error decreased with increase in source-filter interaction level suggesting that the inverse filtering algorithms perform better in the presence of source-filter interaction. All glottal inverse filtering algorithms predicted the open phase ripple factor better than the closed phase ripple factor of a glottal flow waveform, irrespective of the source-filter interaction level. Major prediction errors occurred in the estimation of the closed phase ripple factor, MFDR, peak glottal flow, normalized amplitude quotient, and Quasi-Open Quotient. Feedback-related nonlinearity (source-filter interaction) affected the recovered signal primarily when fo was well below the first formant frequency of a vowel. The prediction error increased when fo was close to the first formant frequency due to the difficulty of estimating the precise value of resonance frequencies, which was exacerbated by nonlinear kinetic losses in the vocal tract.

Glottal Source Information for Pathological Voice Detection

Automatic methods for the detection of pathological voice from healthy speech can be considered as potential clinical tools for medical treatment. This study investigates the effectiveness of glottal source information in the detection of pathological voice by comparing the classical pipeline approach to the end-to-end approach. The traditional pipeline approach consists of a feature extractor and a separate classifier. In the former, two sets of glottal features (computed using the quasi-closed phase glottal inverse filtering method) are used together with the widely used openSMILE features. Using both the glottal and openSMILE features extracted from voice utterances and the corresponding healthy/pathology labels, support vector machine (SVM) classifiers are trained. In building end-to-end systems, both raw speech signals and raw glottal flow waveforms are used to train two deep learning architectures: (1) a combination of convolutional neural network (CNN) and multilayer perceptron (MLP), and (2) a combination of CNN and long short-term memory (LSTM) network. Experiments were carried out using three publicly available databases, including dysarthric (the UA-Speech database and the TORGO database) and dysphonic voices (the UPM database). The performance analysis of the detection system based on the traditional pipeline approach showed best results when the glottal features were combined with the baseline openSMILE features. The results of the end-to-end approach indicated higher accuracies (about 2-3 % improvement in all three databases) when glottal flow was used as the raw time-domain input (87.93 % for UA-Speech, 81.12 % for TORGO and 76.66 % for UPM) compared to using raw speech waveform (85.12 % for UA-Speech, 78.83 % for TORGO and 73.71 % for UPM). The evaluation of both approaches demonstrate that automatic detection of pathological voice from healthy speech benefits from using glottal source information.

Skewing of the glottal flow with respect to the glottal area measured in natural production of vowels.

In the production of voiced speech, glottal flow skewing refers to the tilting of the glottal flow pulses to the right, often characterized as a delay of the peak, compared to the glottal area. In the past four decades, several studies have addressed this phenomenon using modeling of voice production with analog circuits and computer simulations. However, previous studies measuring flow skewing in natural production of speech are sparse and they contain little quantitative data about the degree of skewing between flow and area. In the current study, flow skewing was measured from the natural production of 40 vowel utterances produced by 10 speakers. Glottal flow was measured from speech using glottal inverse filtering and glottal area was captured with high-speed videoendoscopy. The estimated glottal flow and area waveforms were parameterized with four robust parameters that measure pulse skewness quantitatively. Statistical tests obtained for all four parameters showed that the flow pulse was significantly more skewed to the right than the area pulse. Hence, this study corroborates the existence of flow skewing using measurements from natural speech production. In addition, the study yields quantitative data about pulse skewness in simultaneous measured glottal flow and area in natural production of speech.

OPENGLOT – An open environment for the evaluation of glottal inverse filtering

Glottal inverse filtering (GIF) refers to technology to estimate the source of voiced speech, the glottal flow, from speech signals. When a new GIF algorithm is proposed, its accuracy needs to be evaluated. However, the evaluation of GIF is problematic because the ground truth, the real glottal volume velocity signal generated by the vocal folds, cannot be recorded non-invasively from natural speech. This absence of the ground truth has been circumvented in most previous GIF studies by using simple linear source-filter synthesis techniques with known artificial glottal flow models and all-pole vocal tract filters. Moreover, in a few previous studies, physical modeling of speech production has been utilized in synthesis of the test data for GIF evaluation. The evaluation strategy in previous GIF studies is, however, scattered between individual investigations and there is currently a lack of a coherent, common platform to be used in GIF evaluation. In order to address this shortcoming, the current study introduces a new environment, called OPENGLOT, for GIF evaluation. The key ideas of OPENGLOT are twofold: the environment is versatile (i.e., it provides different types of test signals for GIF evaluation) and open (i.e., the system can be used by anyone who wants to evaluate her or his new GIF method and compare it objectively to previously developed benchmark techniques). OPENGLOT consists of four main parts, Repositories I–IV, that contain data and sound synthesis software. Repository I contains a large set of synthetic glottal flow waveforms, and speech signals generated by using the Liljencrants–Fant (LF) waveform as an artificial excitation, and a digital all-pole filter to model the vocal tract. Repository II contains glottal flow and speech pressure signals generated using physical modeling of human speech production. Repository III contains pairs of glottal excitation and speech pressure signal generated by exciting 3D printed plastic vocal tract replica with LF excitations via a loudspeaker. Finally, Repository IV contains multichannel recordings (speech pressure signal, electroglottogram, high-speed video of the vocal folds) from natural production of speech. After presenting these four core parts of OPENGLOT, the article demonstrates the platform by presenting a typical use case.

Direct measurement of glottal flow waveform in an excised K9 larynx with a vocal tract

False vocal folds (FVF) or ventricular folds have been shown to impact the vibration of the true folds. Using computational and experimental models to examine the aerodynamic and acoustic effects the FVF, previous studies have shown that the presence of FVF lowers the phonation threshold pressure, the overall intraglottal pressure distribution, and enhances intraglottal vortical structures. However, the full effect of the FVF on the glottal flow waveform has never been measured in a tissue model of the larynx. Therefore, the objective of this study was to evaluate the impact of FVF on the glottal flow waveform in an excised canine model. A vocal tract model was placed over the larynx, and direct velocity measurements were taken at the glottal exit using tomographic particle image velocimetry. Measurements were taken with a systematic change of the FVF constriction and integrated to calculate the air flow waveform. The results show that a restriction above the folds increased the overall flow rate (dQ/dt) and change the maximum flow declination rate (MFDR). The 3-D measurements at the glottal exit can also be used to validate previous computational studies on how the vocal tract affects the phonation mechanism. The clinical significance of these findings will be further discussed.

Significance of Source–Filter Interaction for Classification of Natural vs. Spoofed Speech

Countermeasures used to detect synthetic and voice-converted spoofed speech are usually based on excitation source or system features. However, in the natural speech production mechanism, there exists nonlinear source–filter (S–F) interaction as well. This interaction is an attribute of natural speech and is rarely present in synthetic or voice-converted speech. Therefore, we propose features based on the S–F interaction for a spoofed speech detection (SSD) task. To that effect, we estimate the voice excitation source (i.e., differenced glottal flow waveform, $\dot{g} (t))$ and model it using the well-known Liljencrants–Fant model to get coarse structure, $g_{c}(t)$ . The residue or difference, $g_{r}(t)$ , between $\dot{g} (t)$ and $g_{c}(t)$ is known to capture the nonlinear S–F interaction. In the time domain, the $L^{2}$ norm of $g_{r}(t)$ in the closed, open, and return phases of the glottis are considered as features. In the frequency domain, the Mel representation of $g_{r}(t)$ showed significant contribution in the SSD task. The proposed features are evaluated on the first ASVspoof 2015 challenge database using a Gaussian mixture model based classification system. On the evaluation set, for vocoder-based spoofs (i.e., S1 – S9 ), the score-level fusion of residual energy features, Mel representation of the residual signal, and Mel frequency cepstral coefficients (MFCC) features gave an equal error rate (EER) of 0.017%, which is much less than the 0.319% obtained with MFCC alone. Furthermore, the residues of the spectrogram (as well as the Mel-warped spectrogram) of estimated $\dot{g} (t)$ and $g_{c}(t)$ are also explored as features for the SSD task. The features are evaluated for robustness in the presence of additive white, babble, and car noise at various signal-to-noise-ratio levels on the ASVspoof 2015 database and for channel mismatch condition on the Blizzard Challenge 2012 dataset. For both cases, the proposed features gave significantly less EER than that obtained by MFCC on the evaluation set.