Auditory Representation Research Articles

Revolutionising patient care: the role of AI-generated avatars in healthcare consultations

Abstract Background/Introduction The healthcare sector faces an unprecedented challenge with the surging demand in both public and private settings, pushing them towards the brink of overload. This scenario underscores a critical knowledge gap in leveraging technology to alleviate the burden on healthcare services. Our study introduces an innovative solution through the development of avatars generated with artificial intelligence (AI) technologies, that replicate the likeness and expertise of healthcare professionals. These AI-generated avatars aim to bridge the gap in patient consultations, offering a scalable and efficient alternative to traditional care models. Purpose The primary objective of our study was to design and implement a virtual assistant capable of conducting patient consultations with the same reliability and personal touch as a trusted human medical professional. This was carried out through the integration of diverse AI technologies including generative models, large language models (LLMs), speech-to-text, and text-to-speech algorithms. Methods Our methodology encompassed the creation of three-components, the visual and auditory representation of the healthcare professional (avatar), the capability to process and generate speech (brain functions), and a database of information sourced from medical repositories as well as expert inputs (compiled knowledge). The development process involved capturing and generating the likeness and voice of a medical expert, training LLMs with the compiled knowledge, and engineering the system to function in real-time. This multifaceted approach ensured the AI-generated avatars were not only personalised but also intelligent and interactive. Results The successful execution of each component confirmed its feasibility, with the AI-generated avatars resembling their human counterparts in appearance and voice, effectively processing spoken inputs, and providing medical advice based on the compiled knowledge. The primary challenge identified was the integration of these components to operate seamlessly in real-time, indicating the need for further refinement in synchronisation and response speed. Conclusion This study has laid a foundational step towards revolutionising patient care through AI technologies. Our results demonstrate the potential of AI-generated avatars to alleviate workloads by providing personalised and efficient patient consultation services. Future work will focus on enhancing the real-time integration of system components, improving the scalability of the solution, and further personalising the patient experience. This direction not only intends to expand the capabilities of healthcare providers but also opens new avenues for patient engagement and care delivery.

Decoding reveals the neural representation of perceived and imagined musical sounds.

Vividly imagining a song or a melody is a skill that many people accomplish with relatively little effort. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts." Here, we decoded perceived and imagined melodies from magnetoencephalography (MEG) brain data (N = 71) to characterize their neural representation. We found that, during perception, auditory regions represent the sensory properties of individual sounds. In contrast, a widespread network including fronto-parietal cortex, hippocampus, basal nuclei, and sensorimotor regions hold the melody as an abstract unit during both perception and imagination. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations, informing future research on neural decoding of auditory imagination.

Auditory, Visual, or Both? Comparing Visual and Auditory Representations of Game Elements in a Gamified Image-Tagging Task

In gamification users show increased motivation and engagement towards tasks. So far, this effect has mostly been empirically tested based on the visual depiction of game elements, while research on the use and addition of auditory aspects is sparse. In this work we investigate the effect of different modalities of game elements (auditory, visual, audiovisual) on user experience, perception and performance in a gamified image-tagging task. We approached this via an online validation survey to find suitable sound effects (N=50), a main quantitative study (N=109) and a qualitative semi-structured interview (N=9). Our results show that while visual gamification increases performance, auditory and audiovisual gamification had no effect. However, they were shown to have an influence on the user's flow state. Our qualitative follow-up study shed light on underlying reasons and revealed each modality has its own drawbacks and advantages and that combining both visual and auditory aspects was preferred by participants.

Interference of mid-level sound statistics underlie human speech recognition sensitivity in natural noise.

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, it's less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds.

Impaired motor-to-sensory transformation mediates auditory hallucinations.

Distinguishing reality from hallucinations requires efficient monitoring of agency. It has been hypothesized that a copy of motor signals, termed efference copy (EC) or corollary discharge (CD), suppresses sensory responses to yield a sense of agency; impairment of the inhibitory function leads to hallucinations. However, how can the sole absence of inhibition yield positive symptoms of hallucinations? We hypothesize that selective impairments in functionally distinct signals of CD and EC during motor-to-sensory transformation cause the positive symptoms of hallucinations. In an electroencephalography (EEG) experiment with a delayed articulation paradigm in schizophrenic patients with (AVHs) and without auditory verbal hallucinations (non-AVHs), we found that preparing to speak without knowing the contents (general preparation) did not suppress auditory responses in both patient groups, suggesting the absent of inhibitory function of CD. Whereas, preparing to speak a syllable (specific preparation) enhanced the auditory responses to the prepared syllable in non-AVHs, whereas AVHs showed enhancement in responses to unprepared syllables, opposite to the observations in the normal population, suggesting that the enhancement function of EC is not precise in AVHs. A computational model with a virtual lesion of an inhibitory inter-neuron and disproportional sensitization of auditory cortices fitted the empirical data and further quantified the distinct impairments in motor-to-sensory transformation in AVHs. These results suggest that "broken" CD plus "noisy" EC causes erroneous monitoring of the imprecise generation of internal auditory representation and yields auditory hallucinations. Specific impairments in functional granularity of motor-to-sensory transformation mediate positivity symptoms of agency abnormality in mental disorders.

Population coding of auditory space in the dorsal inferior colliculus persists with altered binaural cues.

Sound localization is critical for real-world hearing, such as segregating overlapping sound streams. For optimal flexibility, central representations of auditory space must adapt to peripheral changes in binaural cue availability, such as following asymmetric hearing loss in adulthood. However, whether the mature auditory system can reliably encode spatial auditory representations upon abrupt changes in binaural input is unclear. Here we use 2-photon Ca2+ imaging in awake head-fixed mice to determine how the higher-order "shell" layers of the inferior colliculus (IC) encode sound source location in the frontal azimuth, under binaural conditions and after acute monaural hearing loss induced by an ear plug ipsilateral to the imaged hemisphere. Spatial receptive fields were typically broad and not exclusively contralateral: Neurons responded reliably to multiple positions in the contra- and ipsi-lateral hemifields, with preferred positions tiling the entire frontal azimuth. Ear plugging broadened receptive fields and reduced spatial selectivity in a subset of neurons, in agreement with an inhibitory influence of ipsilateral sounds. However ear plugging also enhanced spatial tuning and/or unmasked receptive fields in other neurons, shifting the distribution of preferred angles ipsilaterally with minimal impact on the neuronal population's overall spatial resolution; these effects occurred within 2 hours of ear plugging. Consequently, linear classifiers trained on fluorescence data from control and ear-plugged conditions had similar classification accuracy when tested on held out data from within, but not across hearing conditions. Spatially informative neuronal population codes therefore arise rapidly following monaural hearing loss, in absence of overt experience.

Decoding reveals the neural representation of perceived and imagined musical sounds.

Vividly imagining a song or a melody is a skill that many people accomplish with relatively little effort. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded perceived and imagined melodies from magnetoencephalography (MEG) brain data (N = 71) to characterize their neural representation. We found that, during perception, auditory regions represent the sensory properties of individual sounds. In contrast, a widespread network including fronto-parietal cortex, hippocampus, basal nuclei, and sensorimotor regions hold the melody as an abstract unit during both perception and imagination. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations, informing future research on neural decoding of auditory imagination.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

Math for Everybody: A Sonification Module for Computer Algebra Systems Aimed at Visually Impaired People

Computer Algebra Systems (CAS) currently lack an effective auditory representation, with most existing solutions relying on screen readers that provide limited functionality. This limitation prevents blind users from fully understanding and interpreting mathematical expressions, leading to confusion and self-doubt. This paper addresses the challenges blind individuals face when comprehending mathematical expressions within a CAS environment. We propose “Math for Everybody” (Math4e, version 1.0), a software module to reduce barriers for blind users in education. Math4e is a Sonification Module for CAS that generates a series of auditory tones, prosodic cues, and variations in audio parameters such as volume and speed. These resources are designed to eliminate ambiguity and facilitate the interpretation and understanding of mathematical expressions for blind users. To assess the effectiveness of Math4e, we conducted standardized tests employing the methodologies outlined in the Software Engineering Body of Knowledge (SWEBOK), International Software Testing Qualifications Board (ISTBQ), and ISO/IEC/IEEE 29119. The evaluation encompassed two scenarios: one involving simulated blind users and another with real blind users associated with the “Asociación de Invidentes Milton Vedado” foundation in Ecuador. Through the SAM methodology and verbal surveys (given the condition of the evaluated user), results are obtained, such as 90.56% for pleasure, 90.78% for arousal, and 91.56% for dominance, which demonstrates significant acceptance of the systems by the users. The outcomes underscored the users’ commendable ability to identify mathematical expressions accurately.

Toward an Auditory Virtual Observatory

The large ecosystem of observations generated by major space telescope missions can be remotely analyzed using interoperable virtual observatory technologies. In this context of astronomical big data analysis, sonification has the potential of adding a complementary dimension to visualization, enhancing the accessibility of the archives, and offering an alternative strategy to be used when overlapping issues are found in purely graphical representations. This article presents a collection of sonification and musification prototypes that explore the case studies of the MILES and STELIB stellar libraries from the Spanish Virtual Observatory and the Kepler and TESS light curve databases from the Space Telescope Science Institute archive. Using automation and deep learning algorithms, it offers a “palette” of resources that could be used in future developments oriented toward an auditory virtual observatory proposal. The work includes a user study with quantitative and qualitative feedback from specialized and nonspecialized users analyzing the use of sine waves and musical instrument mappings for revealing overlapped lines in galaxy transmission spectra, confirming the need for training and prior knowledge for the correct interpretation of accurate sonifications, and providing potential guidelines to inspire future designs of widely accepted auditory representations for outreach purposes.

Open Your Ears and Take a Look: A State‐of‐the‐Art Report on the Integration of Sonification and Visualization

AbstractThe research communities studying visualization and sonification for data display and analysis share exceptionally similar goals, essentially making data of any kind interpretable to humans. One community does so by using visual representations of data, and the other community employs auditory (non‐speech) representations of data. While the two communities have a lot in common, they developed mostly in parallel over the course of the last few decades. With this STAR, we discuss a collection of work that bridges the borders of the two communities, hence a collection of work that aims to integrate the two techniques into one form of audiovisual display, which we argue to be “more than the sum of the two.” We introduce and motivate a classification system applicable to such audiovisual displays and categorize a corpus of 57 academic publications that appeared between 2011 and 2023 in categories such as reading level, dataset type, or evaluation system, to mention a few. The corpus also enables a meta‐analysis of the field, including regularly occurring design patterns such as type of visualization and sonification techniques, or the use of visual and auditory channels, showing an overall diverse field with different designs. An analysis of a co‐author network of the field shows individual teams without many interconnections. The body of work covered in this STAR also relates to three adjacent topics: audiovisual monitoring, accessibility, and audiovisual data art. These three topics are discussed individually in addition to the systematically conducted part of this research. The findings of this report may be used by researchers from both fields to understand the potentials and challenges of such integrated designs while hopefully inspiring them to collaborate with experts from the respective other field.

Recent Advances in Synthesis and Interaction of Speech, Text, and Vision

In recent years, there has been increasing interest in the conversion of images into audio descriptions. This is a field that lies at the intersection of Computer Vision (CV) and Natural Language Processing (NLP), and it involves various tasks, including creating textual descriptions of images and converting them directly into auditory representations. Another aspect of this field is the synthesis of natural speech from text. This has significant potential to improve accessibility, user experience, and the applications of Artificial Intelligence (AI). In this article, we reviewed a wide range of image-to-audio conversion techniques. Various aspects of image captioning, speech synthesis, and direct image-to-speech conversion have been explored, from fundamental encoder–decoder architectures to more advanced methods such as transformers and adversarial learning. Although the focus of this review is on synthesizing audio descriptions from visual data, the reverse task of creating visual content from natural language descriptions is also covered. This study provides a comprehensive overview of the techniques and methodologies used in these fields and highlights the strengths and weaknesses of each approach. The study emphasizes the importance of various datasets, such as MS COCO, LibriTTS, and VizWiz Captions, which play a critical role in training models, evaluating them, promoting inclusivity, and solving real-world problems. The implications for the future suggest the potential of generating more natural and contextualized audio descriptions, whereas direct image-to-speech tasks provide opportunities for intuitive auditory representations of visual content.

Frequency effects can modulate the neural correlates of prosodic processing in Mandarin.

In tonal languages, tone perception involves the processing of both acoustic and phonological information conveyed by tonal signals. In Mandarin, in addition to four canonical full tones, there exists a group of weak syllables known as neutral tones. This study aims to investigate the impact of lexical frequency effects and prosodic information associated with neutral tones on the auditory representation of Mandarin compounds. We initially selected disyllabic compounds as targets, manipulating their lexical frequencies and prosodic structures. Subsequently, these target compounds were embedded into selected sentences and auditorily presented to native speakers. During the experiments, participants engaged in lexical decision tasks while their event-related potentials were recorded. The results showed that the auditory lexical representation of disyllabic compounds was modulated by lexical frequency effects. Rare compounds and compounds with rare first constituents elicited larger N400 effects compared to frequent compounds. Furthermore, neutral tones were found to play a role in the processing, resulting in larger N400 effects. Our findings showed significantly increased amplitudes of the N400 component, suggesting that the processing of rare compounds and compounds with neutral tones may require more cognitive resources. Additionally, we observed an interaction effect between lexical frequency and neutral tones, indicating that they could serve as determining cues in the auditory processing of disyllabic compounds.

Sleep deprivation detected by voice analysis.

Sleep deprivation has an ever-increasing impact on individuals and societies. Yet, to date, there is no quick and objective test for sleep deprivation. Here, we used automated acoustic analyses of the voice to detect sleep deprivation. Building on current machine-learning approaches, we focused on interpretability by introducing two novel ideas: the use of a fully generic auditory representation as input feature space, combined with an interpretation technique based on reverse correlation. The auditory representation consisted of a spectro-temporal modulation analysis derived from neurophysiology. The interpretation method aimed to reveal the regions of the auditory representation that supported the classifiers' decisions. Results showed that generic auditory features could be used to detect sleep deprivation successfully, with an accuracy comparable to state-of-the-art speech features. Furthermore, the interpretation revealed two distinct effects of sleep deprivation on the voice: changes in slow temporal modulations related to prosody and changes in spectral features related to voice quality. Importantly, the relative balance of the two effects varied widely across individuals, even though the amount of sleep deprivation was controlled, thus confirming the need to characterize sleep deprivation at the individual level. Moreover, while the prosody factor correlated with subjective sleepiness reports, the voice quality factor did not, consistent with the presence of both explicit and implicit consequences of sleep deprivation. Overall, the findings show that individual effects of sleep deprivation may be observed in vocal biomarkers. Future investigations correlating such markers with objective physiological measures of sleep deprivation could enable "sleep stethoscopes" for the cost-effective diagnosis of the individual effects of sleep deprivation.

A multimodal approach to data analysis in astronomy: sonouno applications in photometry and spectroscopy

ABSTRACT In the past decade, multimodal data analysis has gained importance, especially for including individuals with visual impairments in education and science dissemination. However, its application in scientific research is still limited due to a lack of conclusive evidence on its robustness and performance. Various sonification tools have been developed, including xsonify, starsound, strauss, and sonouno, which aim to enhance accessibility for both sighted and visually impaired users. This contribution presents sonouno (a data visualization and sonification tool) using data, and comparing to corresponding visuals displays, from established data bases like SDSS, ASAS-SN, and Project Clea for astronomical data. We show that sonouno is able to replicate the visual data displays and provide consistent auditory representations. Key features include marking absorption and emission lines (in both visual and sonification) and multicolumn sonification, which facilitates spectral comparisons through sound. This approach ensures consistency between visual and auditory data, making multimodal displays more viable for use in research, enabling greater inclusion in astronomical investigation. The study suggests that sonouno could be broadly adopted in scientific research and used to develop multimodal training courses and improve data analysis methods.

Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions.

Models that predict brain responses to stimuli provide one measure of understanding of a sensory system and have many potential applications in science and engineering. Deep artificial neural networks have emerged as the leading such predictive models of the visual system but are less explored in audition. Prior work provided examples of audio-trained neural networks that produced good predictions of auditory cortical fMRI responses and exhibited correspondence between model stages and brain regions, but left it unclear whether these results generalize to other neural network models and, thus, how to further improve models in this domain. We evaluated model-brain correspondence for publicly available audio neural network models along with in-house models trained on 4 different tasks. Most tested models outpredicted standard spectromporal filter-bank models of auditory cortex and exhibited systematic model-brain correspondence: Middle stages best predicted primary auditory cortex, while deep stages best predicted non-primary cortex. However, some state-of-the-art models produced substantially worse brain predictions. Models trained to recognize speech in background noise produced better brain predictions than models trained to recognize speech in quiet, potentially because hearing in noise imposes constraints on biological auditory representations. The training task influenced the prediction quality for specific cortical tuning properties, with best overall predictions resulting from models trained on multiple tasks. The results generally support the promise of deep neural networks as models of audition, though they also indicate that current models do not explain auditory cortical responses in their entirety.

Sound perception in virtual environments

Virtual environments have been developing and changing the understanding of a space by means design, perception and usage. This study aims to contribute to the literature on the improvement of the auditory perception and cognition of virtual spaces used for education, training, and gaming purposes. This study proposes to offer a realistic representation of soundscapes in virtual environments according to spatial qualities instead of misleading synthetic sounds by integrating acoustical simulations with the immersive environment and questioning the experience of a regular user. The study explores the effects of acoustically simulated and immersive virtual soundscape design methods on auditory perception through changing forms and materials by series of cognitive experiments. The results revealed that the participants achieve more accurate results of source-localization, self-localization, and distance guessing in an immersive environment than in the simulated environment. Also, they were more aware of the soundwalk route, spent more time on tasks, and evaluated the experience more positively in an immersive environment compared to simulations. Despite the placement of the auralizations from the simulations as sound sources in immersive environments, there is still a lack of auditory representation of spatial qualities compared to the accurate calculation of acoustical parameters in a simulated environment.

Temporal coherence of harmonic frequencies affects echo detection in the big brown bat, Eptesicus fuscus.

Echolocating big brown bats (Eptesicus fuscus) broadcast frequency modulated (FM) ultrasonic pulses containing two prominent harmonic sweeps (FM1, FM2). Both harmonics typically return as echoes at the same absolute time delay following the broadcast, making them coherent. Electronically splitting FM1 and FM2 allows their time delays to be controlled separately, making them non-coherent. Earlier work shows that big brown bats discriminate coherent from split harmonic, non-coherent echoes and that disruptions of harmonic coherence produce blurry acoustic images. A psychophysical experiment on two trained big brown bats tested the hypothesis that detection thresholds for split harmonic, non-coherent echoes are higher than those for coherent echoes. Thresholds of the two bats for detecting 1-glint echoes with coherent harmonics were around 35 and 36 dB sound pressure level, respectively, while thresholds for split harmonic echoes were about 10 dB higher. When the delay of FM2 in split harmonic echoes is shortened by 75 μs to offset neural amplitude-latency trading and restore coherence in the auditory representation, thresholds decreased back down to those estimated for coherent echoes. These results show that echo detection is affected by loss of harmonic coherence, consistent with the proposed broader role of coherence across frequencies for auditory perception.

Duplex perception reveals brainstem auditory representations are modulated by listeners' ongoing percept for speech.

So-called duplex speech stimuli with perceptually ambiguous spectral cues to one ear and isolated low- versus high-frequency third formant "chirp" to the opposite ear yield a coherent percept supporting their phonetic categorization. Critically, such dichotic sounds are only perceived categorically upon binaural integration. Here, we used frequency-following responses (FFRs), scalp-recorded potentials reflecting phase-locked subcortical activity, to investigate brainstem responses to fused speech percepts and to determine whether FFRs reflect binaurally integrated category-level representations. We recorded FFRs to diotic and dichotic stop-consonants (/da/, /ga/) that either did or did not require binaural fusion to properly label along with perceptually ambiguous sounds without clear phonetic identity. Behaviorally, listeners showed clear categorization of dichotic speech tokens confirming they were heard with a fused, phonetic percept. Neurally, we found FFRs were stronger for categorically perceived speech relative to category-ambiguous tokens but also differentiated phonetic categories for both diotically and dichotically presented speech sounds. Correlations between neural and behavioral data further showed FFR latency predicted the degree to which listeners labeled tokens as "da" versus "ga." The presence of binaurally integrated, category-level information in FFRs suggests human brainstem processing reflects a surprisingly abstract level of the speech code typically circumscribed to much later cortical processing.

Students’ Cognitive Strategies in Remembering English Vocabulary: Case Study at Ponpes NW Nurul Yaqien Sayang-Sayang Cakranegara Mataram

This research aimed to analyze the student’s cognitive strategies in remembering English Vocabulary survey study at Ponpes NW Nurul Yaqien Sayang-Sayang Cakranegara. The subject of this research was first grade VII-A of MTs NW Nurul Yaqien Sayang-Sayang in academic year 2023/2024. The subject of this study were 7 students. The method of this research was qualitative case study. The technique of data collection applied in this study were interview, recording and note taking/documents. Then the procedure of data analysis was data condensation, data display and conclusion drawing and verification used the theory of Miles and Huberman 2014. The result of this research, the researcher found out the types of student’s cognitive strategies in remembering English vocabulary. The strategies used by the students were Note taking strategy, repeating strategy, Grouping, Recombination, Imagery, Auditory representation, Keyword, Elaboration Transfer, and practice strategy. Therefore, the strategies used by students to remember the meaning and repetition strategy, translation strategy, and auditory representation and dominants used strategy note taking and repetition.