Speech Analysis Techniques Research Articles

Bridging acute and chronic stress effects on inflammation: protocol for a mixed-methods intensive longitudinal study

Acute stress triggers adaptive physiological responses—including transient increases in inflammatory cytokines—while chronic stress is associated with sustained inflammatory activity that may underlie the development of various disorders. Despite extensive research on each stress type individually, the transition and interaction between them remain underexplored. This study aims to address this gap by employing an intensive longitudinal measurement burst design. Healthy university students will be recruited and monitored over three one-week assessment bursts, spaced by three-month breaks. Participants will complete ecological momentary assessments four times daily, recording their emotional states, stress experiences, and daily incidents. Simultaneously, saliva samples will be collected at matching time points to measure biomarkers of immune and stress system activity. In addition, daily audio diaries will provide qualitative context through advanced speech analysis techniques. Data will be analyzed using a multi-level modeling approach to differentiate within-person dynamics from between-person variability, accounting for potential moderators. The findings are expected to shed light on how repeated acute stressors transition into chronic stress and how chronic stress burden may influence acute stress responses.

What If I Mispronounce o Word?: A Linguistic Case Study of Pronunciation Anxiety

Foreign language anxiety is the state of feeling emotional or physical discomfort for language learners (Kralova & Mala, 2018). Language learners’ anxious emotional reaction to a foreign language is often remedied by reducing and balancing the affective filter by language experts and educators (Yaoqing, 2021). From a scientific perspective, the current research indicates that pronunciation anxiety is quite common among language learners regardless of their proficiency levels (Kafes, 2018). Within the same context, this research study specifically investigates the underlying motives that cause pronunciation anxiety among highly-accomplished language learners. A speech analysis technique, an effective method of applied linguistics, is applied to analyze the underlying reasons that cause pronunciation anxiety. This linguistic study hypothesizes that Turkish EFL learners mostly experience pronunciation anxiety in the English language when they come across sounds that are not present in the Turkish language. Thus, it is significant to investigate the impact of the first language interference on pronunciation anxiety levels of language learners through an effective speech analysis technique. This scientific study highlights the correlation between pronunciation anxiety and first language interference. The results indicate that there is not direct correlation between first language interference and pronunciation anxiety; however, the participants of this study showed consistency in the fact that Turkish language learners experience pronunciation difficulty when they come across with sounds that are not present in their native language, Turkish. In parallel, other cross-linguistic interactions across other language combinations can be explored for future research studies

Advanced optimization strategies for combining acoustic features and speech recognition error rates in multi-stage classification of Parkinson's disease severity.

Recent research has made significant progress with definitively identifying individuals with Parkinson's disease (PD) using speech analysis techniques. However, these studies have often treated the early and advanced stages of PD as equivalent, overlooking the distinct speech impairments and symptoms that can vary significantly across the various stages. This research aims to enhance diagnostic accuracy by utilizing advanced optimization strategies to combine speech recognition results (character error rates) with the acoustic features of vowels for more rigorous diagnostic precision. The dysphonia features of three sustained Korean vowels /아/ (a), /이/ (i), and /우/ (u) were examined for their diversity and strong correlations. Four recognized machine-learning classifiers: Random Forest, Support Vector Machine, k-Nearest Neighbors, and Multi-Layer Perceptron, were employed for consistent and reliable analysis. By fine-tuning the Whisper model specifically for PD speech recognition and optimizing it for each severity level of PD, we significantly improved the discernibility between PD severity levels. This enhancement, when combined with vowel data, allowed for a more precise classification, achieving an improved detection accuracy of 5.87% for a 3-level severity classification over the PD "ON"-state dataset, and an improved detection accuracy of 7.8% for a 3-level severity classification over the PD "OFF"-state dataset. This comprehensive approach not only evaluates the effectiveness of different feature extraction methods but also minimizes the variance across final classification models, thus detecting varying severity levels of PD more effectively.

Speaker Diarization: A Review of Objectives and Methods

Recorded audio often contains speech from multiple people in conversation. It is useful to label such signals with speaker turns, noting when each speaker is talking and identifying each speaker. This paper discusses how to process speech signals to do such speaker diarization (SD). We examine the nature of speech signals, to identify the possible acoustical features that could assist this clustering task. Traditional speech analysis techniques are reviewed, as well as measures of spectral similarity and clustering. Speech activity detection requires separating speech from background noise in general audio signals. SD may use stochastic models (hidden Markov and Gaussian mixture) and embeddings such as x-vectors. Modern neural machine learning methods are examined in detail. Suggestions are made for future improvements.

ConceptThread: Visualizing Threaded Concepts in MOOC Videos.

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this article, we propose ConceptThread, a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, speech recognition and topic modeling, to extract core knowledge concepts and construct the hierarchical and temporal relations among them. Then, by using a metaphor of thread, we present a novel visualization to intuitively display the concepts based on video sequential flow, and enable learners to perform interactive visual exploration of concepts. We conducted a quantitative study, two case studies, and a user study to extensively evaluate ConceptThread. The results demonstrate the effectiveness and usability of ConceptThread in providing online learners with a quick understanding of the knowledge content of MOOC videos.

Research Paper on Emotion Recognition

As a recognizing in machine learning algorithm a significant amount of in different various field has been done in many technologies field of machines through which speech has a major impact research interest, especially in the affective computing domain. Increasing potential, algorithmic advancements, and applications in real-world. This human speech contains para-linguistic information that can be represented using different various quantitative features such as pitch, intensity for its deltaic result. It is commonly achieved following three key steps: data processing, feature extraction, and classification based on the underlying emotional features. The nature of these steps, help with the distinct features of human speech, to get the exact result through the underpin with the use of ML methods. Many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Emotion recognition the review covers databases used, emotions extracted, contributions made toward emotion recognition and limitations related to it. signals are an important but challenging component of Human-Computer Interaction (HCI) in machine learning aspect in computer machines through various different perspective and given signals

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis.

Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald's 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients.

Early dementia detection with speech analysis and machine learning techniques

This in-depth study journey explores the context of natural language processing and text analysis in dementia detection, revealing their importance in a variety of fields. Beginning with an examination of the widespread and influence of text data. The dataset utilised in this study is from TalkBank's DementiaBank, which is basically a vast database of multimedia interactions built with the goal of examining communication patterns in the context of dementia. The various communication styles dementia patients exhibit when communicating with others are seen from a unique perspective by this specific dataset. Thorough data preprocessing procedures, including cleansing, tokenization, and structuring, are undertaken, with a focus on improving prediction capabilities through the combination of textual and non-textual information in the field of feature engineering. In the subsequent phase, the precision, recall, and F1-score metrics of Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Random Forest, and Artificial Neural Networks (ANN) are assessed. Empirical facts are synthesized using text analysis methods and models to formulate a coherent conclusion. The significance of text data analysis, the revolutionary potential of natural language processing, and the direction for future research are highlighted in this synthesis. Throughout this paper, readers are encouraged to leverage text data to embark on their own adventures in the evolving, data-centric world of dementia detection.

Automated analysis of speech as a marker of sub-clinical psychotic experiences.

Automated speech analysis techniques, when combined with artificial intelligence and machine learning, show potential in capturing and predicting a wide range of psychosis symptoms, garnering attention from researchers. These techniques hold promise in predicting the transition to clinical psychosis from at-risk states, as well as relapse or treatment response in individuals with clinical-level psychosis. However, challenges in scientific validation hinder the translation of these techniques into practical applications. Although sub-clinical research could aid to tackle most of these challenges, there have been only few studies conducted in speech and psychosis research in non-clinical populations. This work aims to facilitate this work by summarizing automated speech analytical concepts and the intersection of this field with psychosis research. We review psychosis continuum and sub-clinical psychotic experiences, and the benefits of researching them. Then, we discuss the connection between speech and psychotic symptoms. Thirdly, we overview current and state-of-the art approaches to the automated analysis of speech both in terms of language use (text-based analysis) and vocal features (audio-based analysis). Then, we review techniques applied in subclinical population and findings in these samples. Finally, we discuss research challenges in the field, recommend future research endeavors and outline how research in subclinical populations can tackle the listed challenges.

Deep Learning-Based Feature Extraction for Speech Emotion Recognition

Emotion recognition from speech signals is an important and challenging component of Human-Computer Interaction. In the field of speech emotion recognition (SER), many techniques have been utilized to extract emotions from speech signals, including many well-established speech analysis and classification techniques. This model can be built by using various methods such as RNN, SVM, deep learning, cepstral coefficients, and various other methods, out of which SVM normally gives us the highest accuracy. We propose a model that can identify emotions present in the speech, which can be identified by various parameters such as pitch, speaking rate, speech time, and frequency patterns. Emotion detection in digitized speech contains 3 components: Signal processing, Feature extraction, and Classification. The model first tries to remove the background noises then extract the features present in the speech and classify it into a single emotion. This model is capable of identifying seven different emotions that can be found in human speech. We can use different classifiers like GMM and HMM to classify features such as Spectral Subtraction, Wiener Filtering, Adaptive Filtering, and Deep Learning Techniques. This model can be used in various fields such as healthcare, security, psychology, medicine, education, and entertainment.

TERRORISM IN MOZAMBIQUE: ANALYSIS OF THE FACTORS THAT CHALLENGED THE MOZAMBICAN GOVERNMENT ON RESOLUTION OF THE PROBLEM

The year of 2017 marks the beginning of a series of attacks which are considered to be terrorism in Mozambique. It started as a group of militants of unknown identity attacked 3 Mozambican police stations. The attacks carried out by this group resulted in the death of more than 4000 citizens as well as the displacement of more than half a million citizens. This research is important insofar as it seeks to determine the factors that prevented the Mozambican Government from efficiently resolving this problem that led to the suspension of gas exploration projects in the country, a fact that negatively affects its future. Lack of understanding of the problem, lack of experience and equipment to deal with the militants, as well as the focus on military operations, only prevented the Mozambican Government from finding an efficient solution to the situation in Cabo Delgado. In order to carry out the research, speech analysis, documentary and bibliographic techniques are used, since during the research, official documents were used in order to assist the investigation. Moreover, secondary sources, the analysis of works carried out by other researchers, allowed a better understanding of the problem and broadened the scope of the answer to the research problem.

Intelligent Speech Emotion Classification Using Deep Learning Technique

Abstract: Speech emotion recognition is a very interesting but very challenging human-computer interaction task. In recent years, this topic has attracted a lot of attention. In the field of speech emotion recognition, many techniques have been used to extract emotion from signals, including many well-established speech analysis and classification techniques. In the traditional way of speech emotion recognition, the emotion recognition features are extracted from the speech signals, and then the features, which are collectively known as the selection module, are selected, and then the emotions are recognized, which is a very tedious and time-consuming process, so this paper provides an overview of the deep learning technique, which is based on a simple algorithm based on feature extraction and building a model that recognizes emotions

SPEECH EMOTION RECOGNITION WITH FACIAL EXPRESSIONS

The speech emotion recognition is a very interesting yet very challenging task of human computer interaction. In the recent years this topic has grabbed so much attention. In the field of speech emotion recognition many techniques have been utilized to extract emotions from signals , including many well- established speech analysis and classification techniques. In the traditional way of speech emotion recognition features are extracted from the speech signals and then the features are selected which is collectively know as selection module and then the emotions are recognized this is a very lengthy and time taking process. To understand the importance of Speech emotion & facial expression and how it is important for emotion detection. Emotion detection from our project has vast study for further future research and development. To build automatically speech and facial expression recognition has an effective emerging for developments. This project extends study and development on emotion detection with speech and facial expressions using Convolution Neural network (CNN). This task is done by detecting the facial actions per every unit of face measurements as a sub part of facial action coding system. This project offers light on utilization of CNN from a live video stream as an input. Using various machine learning libraries like tensor flow and many more. With this development it's advantageous to various domains such as medical engineering, technology, marketing etc.. Key words:- Emotion , Features , Facial expressions , Speech , Techniques , Technology.

Interpreting acoustic emissions to determine the weld depth during laser beam welding

The interpretation of sensor system data is critical for monitoring industrial welding processes and providing reliable information about the condition of the weld seam. Previous investigations have shown that acoustic emissions of frequencies up to several kilohertz during laser beam welding are parameter-dependent and contain valuable information about the process. A microphone was employed to record the acoustic emissions produced when performing deep penetration laser beam welding of copper. Experiments were conducted in which the laser power and the feed rate were varied so as to obtain acoustic data comprising frequencies of up to 1 MHz. The signals were preprocessed and features were extracted using Fourier and wavelet analysis as well as speech analysis techniques. The relationship between the features extracted from the acoustic signal and the weld depth was modeled using Gaussian process regression. The results showed that acoustic emissions during laser beam welding can be used to predict the weld depth without having to rely on process parameters, i.e., the laser power and the feed rate. Overall, 17 features were extracted from acoustic signals, with the zero-crossing rate displaying the highest significance for determining the weld depth. These investigations open up new possibilities of robust quality assurance for laser beam welding applications based on acoustic emissions.

A Linguistic Analysis Study for Teaching American-English Pronunciation: A Case Study Research

Abstract This linguistic case study research investigates the process stages of a language learner’s pronunciation errors through speech analysis detection techniques. The purpose of this study is to detect and analyze the pronunciation errors for internalizing the unique and individual pronunciation error patterns that vary from one individual to the next. A speech analysis technique, a method of applied linguistics, is used in combination with the case study method reveals a thorough evaluation map for the language learner’s pronunciation error characteristics. In this case study, the participant of the study is a native Chinese speaker. Within the scope of this research study, both the theoretical literature review and the data collection process uncover that pronunciation, unlike other domains of language teaching areas, requires specific and individualized attention for effective teaching and learning strategies. The data collected in this research study is analyzed in terms of phonetic linguistic features, and a personalized pronunciation improvement plan is proposed based on the linguistic features and characteristics of the research participant’s errors. A larger scale research study can be conducted for future research by verifying the findings of this research study with more language-specific research participants in order to develop individualized and learning-style focused pronunciation lesson plans for larger learning groups.

LANGaware: Introducing the right solution for the early detection of neurodegenerative and psychiatric diseases

Abstract BackgroundThere are multiple neurodegenerative diseases that directly affect speech [1]. However, its utilization as a robust indicator for cognitive impairment is under‐investigated. In many cases, mild cognitive decline progresses to a neurodegenerative disease and its detection is of utmost importance, since it is at this stage that treatment is most effective. One of our core goals is developing techniques for differentiating between patients with cognitive decline and healthy cohorts, by utilizing only speech samples [2]. Such samples are obtained from verbal elicitation tasks designed for cognitive assessment, e.g. picture descriptions and narration of everyday activities.MethodAudio recordings from cognitive assessment tasks are fed through our platform to a Natural Language Processing and Machine Learning pipeline, employing an automatic discovery procedure of predictive salient biomarkers, to train an advanced classification system. The biomarker collection includes features that characterize voice, speech, language structure, composition and usage, and are engineered to highlight symptoms of neurodegenerative disorders. Biomarkers undergo multiple stages of filtering, processing and transformation to train and fine‐tune the final model. Diagnostic performance of the output classifier is obtained on an unseen test set, to ensure a robust generalization of the platform.ResultOur platform utilizes the set of automatically selected, cross‐linguistic digital biomarkers to obtain sensitivity and specificity scores of 81% and 84% respectively, compared against medical expert diagnosis. The most salient biomarkers with respect to identifying our pathological cohort, relate to feature categories of Syntactic Complexity, Content Word usage, Lexical Repetition, Syntactic Errors and Function Words, with weight contributions of ∼ 16%, 14%, 13%, 12% and 11% respectively. Our platform provides additional, detailed population and patient‐based descriptive analytics to enhance transparency and explainability of the results.ConclusionEarly detection of cognitive decline facilitates early intervention, treatment and proactive care, delaying disease progression and reducing symptom severity. We believe that our platform provides an effective solution for risk factor estimation and our findings incentivize further research into speech analysis techniques for the prediction of cognitive decline.[1] Boschi, Veronica, et al.,Frontiers in psychology 8 (2017): 269.[2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020.

Euphemism Forms in the Deddy Corbuzier’s Youtube Account Comment Column

This study aims to describe the form of euphemism in the comments column of Deddy Corbuzier's Youtube account. This type of research is descriptive qualitative. The data collection technique in this study was the listening technique and the note-taking technique. This study uses a content analysis approach. The data from this study are expressions in the form of words, sentences, and phrases that contain euphemisms contained in the comments column of Deddy Corbuzier's Youtube account. Speech analysis technique in this study uses the equivalent method. This study found 25 data in the form of euphemistic expressions contained in the comments column of Deddy Corbuzier's Youtube account. From the 25 data, there are 10 forms of euphemisms, namely: (1) metaphorical euphemisms where words are ignored and like knights, (2) figurative forms of expression euphemisms include the word natural selection and carrying out a mandate, (3) circumlocutionary euphemisms include the word children of the nation, formality. , and extraordinary, (4) euphemisms in the form of idiomatic phrases include humble words, brilliant thoughts, rare, and right-handed, (5) euphemisms in hyperbole form include words bitter, salty and enlightening thoughts, (6) euphemisms in the form of leasing have formula words ( special) and (angel) protectors, (7) euphemisms in the form of foreign terms include the words low profile, legowo and smart people, (8) euphemisms in the form of one word replacing another word with the word wow, building generations, and begging, (9) euphemisms of the form uptake contains the words salute and appreciation, and (10) euphemisms in the abbreviated form include the words UKT and PJJ.

Improved Classification with Simultaneous Feature Selection of Speech Signals for Parkinson's Patients

Parkinson's disease (PD) is a brain disorder occurs due to a deficiency of dopamine hormone that regulates activities of the human body. Generally, the disease can be diagnosed by clinicians through clinical observation where they categorized PD patients on a PD assessment scale to understand disease severity in order to define a therapy/treatment plan. The clinicians have a view that this approach is not suitable for diagnosis at an early stage of the disease. Recent research outcome has shown that PD patients exhibit vocal impairment at the early stage of the disease, and this is now becoming a benchmark for early PD detection. Often researchers employ state-of-the-art speech analysis techniques that exploit various extracted features to meet the objective. An optimal set of features that best explains the problem often requires careful attention to the selection of extracted features in use. As a general practice, data analysts have a view that it is better to collect as many features as possible related to the problem but at the same time, it is also believed that the presence of some noisy features can also compromise classification ability. Our main objective in this work is to select/identify the optimal set of features to utilize for the machine learning classification models with an objective to have an improved early PD detection in patients. The selection of optimal features set will not only help clinicians to quickly diagnose PD but will also be useful to develop a better patient care strategy at an early stage of PD. In this study, various experiment are conducted to observe the most contributing speech feature to classify PD patients. The study have showed by using the Best-First feature selection approach the most optimal features from the PD dataset can be achieved. The efficacy of our approach with the optimal set of features has shown an improvement in classification with an accuracy of 92.19% that is better than the earliest reported accuracy of 86% [23] for an almost similar number of features.

Optimising Speaker-Dependent Feature Extraction Parameters to Improve Automatic Speech Recognition Performance for People with Dysarthria

Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker’s voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker’s voice features and their optimal parameters.

Implementation of automatic speech analysis for early detection of psychiatric symptoms: What do patients want?

Psychiatry is in dire need of a method to aid early detection of symptoms. Recent developments in automatic speech analysis prove promising in this regard, and open avenues for implementation of speech-based applications to detect psychiatric symptoms. The current survey was conducted to assess positions with regard to speech recordings among a group (n = 675) of individuals who experience psychiatric symptoms. Overall, respondents are open to the idea of speech recordings in light of their mental welfare. Importantly, concerns with regard to privacy were raised. Given that speech recordings are privacy sensitive, this requires special attention upon implementation of automatic speech analysis techniques. Furthermore, respondents indicated a preference for speech recordings in the presence of a clinician, as opposed to a recording made at home without the clinician present. In developing a speech marker for psychiatry, close collaboration with the intended users is essential to arrive at a truly valid and implementable method.