Vocal Cord Vibration Research Articles

Stress-deconcentrated ultrasensitive strain sensor with hydrogen-bonding-tuned fracture resilience for robust biomechanical monitoring

Recently, rapid advances in flexible strain sensors have broadened their application scenario in monitoring of various mechanophysiological signals. Among various strain sensors, the crack-based strain sensors have drawn increasing attention in monitoring subtle mechanical deformation due to their high sensitivity. However, early generation and rapid propagation of cracks in the conductive sensing layer result in a narrow working range, limiting their application in monitoring large biomechanical signals. Herein, we developed a stress-deconcentrated ultrasensitive strain (SDUS) sensor with ultrahigh sensitivity (gauge factor up to 2.3 × 106) and a wide working range (0%–50%) via incorporating notch-insensitive elastic substrate and micro-crack-tunable conductive layer. Furthermore, the highly elastic amine-based polymer-modified polydimethylsiloxane substrate without obvious hysteresis endows our SDUS sensor with a rapid response time (2.33 ms) to external stimuli. The accurate detection of the radial pulse, joint motion, and vocal cord vibration proves the capability of SDUS sensor for healthcare monitoring and human-machine communications.

Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2

This paper proposes a framework of applying only the EGG signal for speech synthesis in the limited categories of contents scenario. EGG is a sort of physiological signal which can reflect the trends of the vocal cord movement. Note that EGG’s different acquisition method contrasted with speech signals, we exploit its application in speech synthesis under the following two scenarios. (1) To synthesize speeches under high noise circumstances, where clean speech signals are unavailable. (2) To enable dumb people who retain vocal cord vibration to speak again. Our study consists of two stages, EGG to text and text to speech. The first is a text content recognition model based on Bi-LSTM, which converts each EGG signal sample into the corresponding text with a limited class of contents. This model achieves 91.12% accuracy on the validation set in a 20-class content recognition experiment. Then the second step synthesizes speeches with the corresponding text and the EGG signal. Based on modified Tacotron-2, our model gains the Mel cepstral distortion (MCD) of 5.877 and the mean opinion score (MOS) of 3.87, which is comparable with the state-of-the-art performance and achieves an improvement by 0.42 and a relatively smaller model size than the origin Tacotron-2. Considering to introduce the characteristics of speakers contained in EGG to the final synthesized speech, we put forward a fine-grained fundamental frequency modification method, which adjusts the fundamental frequency according to EGG signals and achieves a lower MCD of 5.781 and a higher MOS of 3.94 than that without modification.

A facile method combined with electroless nickel plating and carbonization to fabricate textured Ni-coated carbon tube for flexible strain sensor

High-sensitive textile sensor acts the key component of electronic wearable products. It is a challenge to low-cost fabricate flexible sensors that possess excellent response reliability. In this regard, we employ a facile method combined with electroless nickel plating and carbonization to fabricate textured Ni-coated carbon tube (Te-CNiT) electrode, used as sensitive material for resistive sensor. Results showed that active group on the cotton surface would facilitate the adsorption of Pd2+. The generated Pd particle served as catalytic center to catalyze the electroless nickel plating by means of core-sheath micronfiber yarn with weave structure. Moreover, the “core” layer is transited from plant fibre to amorphous carbon which attached to the inside of Ni tube after annealing at 300 °C for 1 h, as a result, Te-CNiT electrode was fabricated. Interestingly, the change relationship between the relative resistance and strain present a quadratic equation of one variable, more specifically, the dependent variable varies obviously with the independent variable due to the established of extra conductor resistance and contact resistance in the Te-CNiT under the action of strain. As expected, the developed sensor exhibited ultrahigh sensitivity with a detection limit of 0.3% and gauge factor of 359. It can be integrated with the human body to detect multiple tiny signals, such as body movement, vocal cord vibration and facial expression with low response/recovery time (0.06 s/0.04 s) and excellent stability (9000 cycles).

Stretchable, compressible, and conductive hydrogel for sensitive wearable soft sensors

Conductive hydrogels hold great promises in wearable soft electronics. However, the weak mechanical properties, low sensitivity and the absence of multifunctionalities (e.g., self-healing, self-adhesive, etc.) of the conventional conductive hydrogels limit their applications. Thus, developing multifunctional hydrogels may address some of these technical issues. In this work, a multifunctional conductive hydrogel strain sensor is fabricated by incorporating a conductive polymer Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT: PSS) into a mechanically robust poly (vinyl alcohol) (PVA)/ poly (acrylic acid) (PAA) double network (DN) hydrogel. The as-prepared hydrogel sensor could span a wide spectrum of mechanical properties by simply tuning the polymer composition and the number of freezing-thawing cycles. In addition, the dynamic hydrogen bonding interactions endow the hydrogel sensor with self-healing property and reversible adhesiveness on diverse substrates. Moreover, the hydrogel sensor shows high sensitivity (Gauge Factor from 2.21 to 3.82) and can precisely detect some subtle human motions (e.g., pulse and vocal cord vibration). This work provides useful insights into the development of conductive hydrogel-based wearable soft electronics.

Color‐Customizable, Stretchable, Self‐Healable and Degradable Ionic Gel for Variable Human‐Motion Detection via Strain, Pressure, and Torsion

AbstractRecently, flexible sensors have attracted great interest for their promising application in wearable electronics. However, previous mechanical sensors are typically limited to detecting one type of stimulus and monotonous in color, which are not able to meet the sensing nor fashion requirements by customers who will wear them in daily life. Herein, a flexible and stretchable sensor based on highly conductive ionic gel is developed. Different phosphor nanoparticles are uniformly embedded into gel to enable color‐customization and even self‐illumination depending on preference. The gel is also self‐healable after complete severance and degradable in hot water. The sensors can detect multiple mechanical deformation modes to allow for detections of strain, pressure, and torsion on a single device. Wearable sensing prototypes are demonstrated with attachment to different parts of human body to detect various human motions, including strain sensing for frowning and elbow/knee bending, pressure sensing for vocal cord vibrations and pulse at wrist, and torsion sensing for shoulder and forearm rotations, through an in‐house, wireless, real‐time signal transmission and process system. Considering its sensing capability toward different stimuli as well as superior physical properties, the developed color‐customizable and self‐illuminating ionic gel sensor would find its promising applications toward wearable sensing.

Solid-state intrinsically-superstretchable multifunctional nanogenerator fiber for biomechanical and ambient electromagnetic energy harvesting and self-powered sensing

Developing energy devices that are soft but tough and can extract multiple forms of surrounding energy is a crucial challenge for sustainable use of wearable electronics. Here, we report a solid-state naturally superstretchable (~590% stretchability) multifunctional nanogenerator fiber that can scavenge both biomechanical energy (e.g., from body motion) and dissipated electromagnetic energy (e.g., from nearby electrical appliances). Not only collect two types of waste energy, but also it can act as self-powered sensor. It comprises a conducting composite-coated extensible fiber clad in a triboelectric elastomer and extracts energy through triboelectrification (424 V m−1, 9.53 µA m−1, and ~85.2 µW m−1) and electromagnetic induced electrification ( ± 8.8 V m−1, ± 1.6 μA m−1, and ~2.81 μW m−1 at 60 Hz). The behavior to harvest nearby electromagnetic energy is successfully demonstrated in a carbon nanotubes (CNTs)-based composite. The collected energy can power electronic gadgets. Moreover, it can transmit biomechanical information, including physiological/respiratory signals, vibration of vocal cords, gestures, and touching, via self-generated electricity. The fibers are further used as self-powered human-machine interfaces in system-level smart clothing. The fiber that unites mechanical freedom and the ability of capturing multiple forms of energy and self-powered sensing can meet vast application needs for wearable/stretchable/personal energy and sensing technologies.

Comparison of enhancement techniques based on neural networks for attenuated voice signal captured by flexible vibration sensors on throats

Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech. However, high-frequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction. In this paper, speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared. Four kinds of speech enhancement algorithms based on a fully connected neural network (FCNN), a long short-term memory (LSTM), a bidirectional long short-term memory (BLSTM), and a convolutional-recurrent neural network (CRNN) are adopted to enhance the sensor signals, and their performance after deployment on four kinds of edge and cloud platforms is also investigated. Experimental results show that the BLSTM performs best in improving speech quality, but is poorest with regard to hardware deployment. It improves short-time objective intelligibility (STOI) by 0.18 to nearly 0.80, which corresponds to a good intelligibility level, but it introduces latency as well as being a large model. The CRNN, which improves STOI to about 0.75, ranks second among the four neural networks. It is also the only model that is able to achieves real-time processing with all four hardware platforms, demonstrating its great potential for deployment on mobile platforms. To the best of our knowledge, this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors. The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.

Integrated Wearable Sensors for Sensing Physiological Pressure Signals and β-Hydroxybutyrate in Physiological Fluids.

Flexible and wearable sensors have attracted much attention for their applications in health monitoring and the human-machine interaction. The most studied wearable sensors have been demonstrated for sensing a limited range of metabolites such as ions, glucose, uric acid, lactate, etc. Both sweat and urine contain numerous other physiologically relevant metabolites indicative of health and wellness. This work demonstrates the use of the wearable sensor for the detection of β-hydroxybutyrate (HB) in sweat. HB is an important biomarker for diabetic ketoacidosis, a condition caused by the accumulation of ketone bodies in hyperglycemia or metabolic acidosis patients. Herein, we fabricated an integrated sensing system coupling an HB detection chamber with a serpentine electrode for sensing physiological signals such as pulse beat, vocal cord vibration, etc. The real-time HB detection was based on a β-hydroxybutyrate dehydrogenase enzymatic reaction. The stability of the enzyme and the cofactor couple was achieved by cross-linking networks and a redox mediator, thereby achieving high selectivity and low detection limits to HB in urine and sweat. The dual-functional sensor was integrated with a signal processing circuitry for signal transduction, conditioning, processing, wireless transmission, and real-time convenient health monitoring display to a smartphone via home-developed software.

Design of a Superhydrophobic Strain Sensor with a Multilayer Structure for Human Motion Monitoring.

A flexible strain sensor is of significant importance in wearable electronics since it can help monitor the physical signals from the human body. Among various strain sensors, the polyurethane (PU)-based ones have received widespread attention owing to their excellent toughness, large working range, and nice gas permeability. However, the hydrophobicity of these sensors is not good enough, which may affect their use life and sensitivity. In this work, a high-performance strain sensor composed of PU, reduced graphene oxide (rGO), polydopamine (PDA), and 1H,1H,2H,2H-perfluorodecane-thiol (PFDT) was designed and prepared. The results revealed that this PU/rGO/PDA/PFDT device possessed good superhydrophobicity with a water contact angle of 153.3°, a wide working strain range of 590%, and an outstanding gauge factor as high as 221 simultaneously. Because of these above advantages, the sensor worked effectively in detecting both subtle and large human movements (such as joint motion, finger motion, and vocal cord vibration) even in a high humidity environment. This strain sensor with high sensitivity, wide working range, and suitable modulus may have great potential in the field of flexible and wearable electronics in the near future.

Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure

ObjectivesThis study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF). BackgroundPulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics. MethodsAdult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated. ResultsIn total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs. ConclusionsAutomated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029)

Voice pathology detection and classification from speech signals and EGG signals based on a multimodal fusion method.

Automatic voice pathology detection and classification plays an important role in the diagnosis and prevention of voice disorders. To accurately describe the pronunciation characteristics of patients with dysarthria and improve the effect of pathological voice detection, this study proposes a pathological voice detection method based on a multi-modal network structure. First, speech signals and electroglottography (EGG) signals are mapped from the time domain to the frequency domain spectrogram via a short-time Fourier transform (STFT). The Mel filter bank acts on the spectrogram to enhance the signal's harmonics and denoise. Second, a pre-trained convolutional neural network (CNN) is used as the backbone network to extract sound state features and vocal cord vibration features from the two signals. To obtain a better classification effect, the fused features are input into the long short-term memory (LSTM) network for voice feature selection and enhancement. The proposed system achieves 95.73% for accuracy with 96.10% F1-score and 96.73% recall using the Saarbrucken Voice Database (SVD); thus, enabling a new method for pathological speech detection.

Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training.

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Programmable patterned MoS2 film by direct laser writing for health-related signals monitoring

SummaryThe two-dimensional (2D) transition metal dichalcogenides (TMDs) are promising flexible electronic materials for strategic flexible information devices. Large-area and high-quality patterned materials were usually required by flexible electronics due to the limitation from the process of manufacturing and integration. However, the synthesis of large-area patterned 2D TMDs with high quality is difficult. Here, an efficient and powerful pulsed laser has been developed to synthesize wafer-scale MoS2. The flexible strain sensor was fabricated using MoS2 and showed high performance of low detection limit (0.09%), high gauge factor (1,118), and high stability (1,000 cycles). Besides, we demonstrated its applications in real-time monitoring of health-related physiological signals such as radial artery pressure, respiratory rate, and vocal cord vibration. Our findings suggest that the laser-assisted method is effective and capable of synthesizing wafer-scale 2D TMDs, which opens new opportunities for the next flexible electronic devices and wearable health monitoring.

Polyimide Nanofiber-Reinforced Ti3C2Tx Aerogel with "Lamella-Pillar" Microporosity for High-Performance Piezoresistive Strain Sensing and Electromagnetic Wave Absorption.

Assembling two-dimensional MXenes into 3D macroscopic structures is an applicable method to give full play to its excellent electrical and mechanical properties toward multi-functionality. Considering the weak interfacial interaction and poor gelation ability of MXenes, short polyimide nanofibers (PINFs) are utilized as cross-linking and supporting building blocks in this work to construct a lightweight, robust, and elastic PINF/Ti3C2Tx MXene composite aerogel (PINF/MA) via a simple synergistic assembly strategy. Taking advantage of its unique 3D "lamella-pillar" microporous architecture, the designed PINF/MA composite aerogel exhibits excellent piezoresistive sensing performance in terms of a wide pressure range of 0-8 kPa (50% strain), a high piezoresistive sensitivity of 22.32 kPa-1, an ultra-low detection limit of 0.1% strain, and great compression/rebound stability (signal remained stable after 1500 cycles). These remarkable piezoresistive sensing properties enable the PINF/MA with intriguing capability to detect small and large human activities in real time (wrist and finger bending, pulse, and vocal cord vibration). More interestingly, the parallelly aligned leaf vein-like lamellae also empower the PINF/MA with prominent wave absorption performance [RLmin is -40.45 dB at 15.19 GHz, with an effective absorption bandwidth of 5.66 GHz (12.34-18 GHz)], making the multi-functional PINF/MA composite aerogels promising candidates for wearable strain sensors and microwave absorbers.

Highly Strong, Stretchable, and Conductive Reduced Graphene Oxide Composite Hydrogel-Based Sensors for Motoring Strain and Pressure

Conductive hydrogels have shown great potential for fabricating flexible and wearable sensors, especially strain/pressure sensors to monitor human motion/health. In practice, the use of conductive hydrogel-based sensors is often limited because of hydrogels’ poor mechanical properties and low sensitivity. In the present article, a composite hydrogel comprising acrylamide, N-(3-aminopropyl) methyl acrylamide hydrochloride, and polydopamine-coated reduced graphene oxide is prepared. The dynamic covalent and noncovalent interactions that exist in the hydrogel matrix including C–N bonds, intra-/intermolecular hydrogen bonds and NH3+–π interaction, and π–π interactions rendered the hydrogels with superior fracture stress (496.4 ± 85.9 kPa) and ductility (1156.9 ± 110.1%). Also, prepared hydrogels have excellent cytocompatibility and show appropriate adhesion to the skin, showcasing their potential in fabricating wearable devices. On the basis of these facts, hydrogel-based strain/pressure sensors that cause a change in resistance when strain or pressure is applied are fabricated. The fabricated sensors show a high strain sensitivity in the strain range of 0–800% where, at a subtle strain (100%), the gauge factor reached 5.4. In addition, sensors are also responsive to pressure with a sensitivity of −0.74% kPa–1 in the range from 0 to 10 kPa. The fabricated strain/pressure sensors have the cyclic ability and self-healing property at room temperature. As a proof-of-concept, the sensors are shown to monitor movements of the elbow, knee, fingers, and wrists as well as sense the vocal cord vibration.

A Highly Stretchable and Sensitive Strain Sensor Based on Dopamine Modified Electrospun SEBS Fibers and MWCNTs with Carboxylation

AbstractWearable flexible electronic strain sensor devices have developed rapidly in recent years due to their potential capacity to detect human motion in various situations. However, it still remains still a big challenge to fabricate strain sensors with high sensitivity over a wide workable strain range. In order to meet this challenge, a new type of strain sensor based on elastomer/carbon nanotube composite fiber is reported in this work. Elastomer fibers are initially prepared via the electrospinning of styrene ethylene butene styrene block copolymer (SEBS). The resultant SEBS fibers are then functionalized by sequentially coating with dopamine (DA) coating and carboxyl group (‐COOH) grafted multi‐walled carbon nanotubes (MWCNTs) under vacuum filtration and ultrasonication. Scanning electron microscopy (SEM) and thermogravimetric analysis reveals that a large amount of MWCNTs is firmly bonded onto the SEBS fibers and evenly distributed. SEBS@PDA/MWCNTs composite fibers based strain sensors exhibit excellent performance, including a high gauge factor of 3717 and large workable strain range up to 530%. Furthermore, the developed sensors demonstrate excellent washing fastness and superior sensitivity in monitoring both small strains (e.g., pulse beats and vocal cord vibrations) and large strains (e.g., finger, elbow, and knee bending).

High-Performance Foam-Shaped Strain Sensor Based on Carbon Nanotubes and Ti3C2Tx MXene for the Monitoring of Human Activities.

The flexible strain sensor is of significant importance in wearable electronics, since it can help monitor the physical signals from the human body. Among various strain sensors, the foam-shaped ones have received widespread attention owing to their light weight and gas permeability. However, the working range of these sensors is still not large enough, and the sensitivity needs to be further improved. In this work, we develop a high-performance foam-shaped strain sensor composed of Ti3C2Tx MXene, multiwalled carbon nanotubes (MWCNTs), and thermoplastic polyurethane (TPU). MXene sheets are adsorbed on the surface of a composite foam of MWCNTs and TPU (referred to as TPU/MWCNTs foam), which is prefabricated by using a salt-templating method. The obtained TPU/MWCNTs@MXene foam works effectively as a lightweight, easily processable, and sensitive strain sensor. The TPU/MWCNTs@MXene device can deliver a wide working strain range of ∼100% and an outstanding sensitivity as high as 363 simultaneously, superior to the state-of-the-art foam-shaped strain sensors. Moreover, the composite foam shows an excellent gas permeability and suitable elastic modulus close to those of skin, indicating its being highly comfortable as a wearable sensor. Owing to these advantages, the sensor works effectively in detecting both subtle and large human movements, such as joint motion, finger motion, and vocal cord vibration. In addition, the sensor can be used for gesture recognition, demonstrating its perspective in human-machine interaction. Because of the high sensitivity, wide working range, gas permeability, and suitable modulus, our foam-shaped composite strain sensor may have great potential in the field of flexible and wearable electronics in the near future.

Consonant voicing in the Buckeye corpus.

Many claims about the prevalence of phonetic voicing in English obstruents have been made in the literature over the decades, particularly concerning the stops and affricate [b, d, ɡ, ʤ]. An examination of this literature reveals that many of these claims are based on a paucity of speech data and measurements. For the present study, voiced consonants in the Buckeye corpus of American English (39 speakers) have been measured to determine the percentage of their duration that shows vocal cord vibrations. The prevalence of voicing in the 53 690 voiced stop and affricate tokens has been examined in all contexts, including the initial, intervocalic, and final positions. The results generally contradict the common notion that the nominally "voiced" stops of English are phonetically unvoiced in all positions but intervocalic. Here, they are found to be mostly voiced in final position as well as intervocalically, but usually less than 50% voiced in initial position. A significant proportion of these stops, however, were found to be nearly 100% voiced in the initial position, and this could not be explained by interspeaker variation.

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor

Electronic skins that can sense external stimuli have been of great significance in artificial intelligence and smart wearable devices in recent years. However, most of current skin materials are unable to achieve high biocompatibility and anti-bacterial activity, which are particularly critical to wearable sensors for neonatal/premature monitoring or tissue-interfaced biosensors (such as electronic wound dressing and smart contact lens). Herein, a zwitterionic-aromatic motif-based conductive hydrogel with electrostatic and π-π interactions is designed for the development of ionic skin sensors. The hydrogel possesses high biocompatibility, anti-bacterial activity, especially glucose-responsive property which has not been achieved by previous ionic skins. Due to its unique molecular design, the zwitterionic-aromatic skin sensor exhibits excellent mechanical properties (robust elasticity and large stretchability) and high-sensitive pressure detection (including a gentle finger touch, small water droplets, and vocal cord vibration). More importantly, aromatic motives in phenylboronic acid segments endow the skin with glucose-responsive property. This skin sensor not only shows great potential in wearable e-skins, but also possesses a promising property for the tissue-interfaced and implantable continuous-glucose-monitor biosensors such as smart wound dressing with a high demand of biocompatibility.

Effects of Sulcus Vocalis Depth on Phonation in Three-Dimensional Fluid-Structure Interaction Laryngeal Models.

Sulcus vocalis is an indentation parallel to the edge of vocal fold, which may extend into the cover and ligament layer of the vocal fold or deeper. The effects of sulcus vocalis depth d on phonation and the vocal cord vibrations are investigated in this study. The three-dimensional laryngeal models were established for healthy vocal folds (0 mm) and different types of sulcus vocalis with the typical depth of 1 mm, 2 mm, and 3 mm. These models with fluid-structure interaction (FSI) are computed numerically by sequential coupling method, which includes an immersed boundary method (IBM) for modelling the glottal airflow, a finite-element method (FEM) for modelling vocal fold tissue. The results show that a deeper sulcus vocalis in the cover layer decreases the vibrating frequency of vocal folds and expands the prephonatory glottal half-width which increases the phonation threshold pressure. The larger sulcus vocalis depth makes vocal folds difficult to vibrate and phonate. The effects of sulcus vocalis depth suggest that the feature such as phonation threshold pressure could assist in the detection of healthy vocal folds and different types of sulcus vocalis.