Speech Analysis Research Articles

INTEGRATING AI-BASED SPEECH RECOGNITION TECHNOLOGY TO ENHANCE READING ASSESSMENTS WITHIN MOROCCO’S TaRL PROGRAM

This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco’s Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper- based assessments in improving reading skills among 100 Moroccan first to third-graders. The mixed- method approach combined pre-post standardized reading tests with qualitative feedback. Results showed students receiving the AI-enabled speech recognition assessments demonstrated significant gains in reading achievement compared to peers assessed via traditional methods. Qualitative findings revealed benefits of instant feedback and enhanced engagement provided by the speech recognition tool. This study contributes timely empirical evidence on adopting learning technologies, specifically AI-driven automated speech assessment instruments, to enhance foundational literacy development within under-resourced education systems implementing student-centered pedagogical techniques like TaRL. It provides valuable insights and guidance for integrating innovative speech analysis tools within localized teaching and learning frameworks to strengthen early reading instruction and monitoring.

Smartphone automated motor and speech analysis for early detection of Alzheimer's disease and Parkinson's disease: Validation of TapTalk across 20 different devices.

Smartphones are proving useful in assessing movement and speech function in Alzheimer's disease and other neurodegenerative conditions. Valid outcomes across different smartphones are needed before population-level tests are deployed. This study introduces the TapTalk protocol, a novel app designed to capture hand and speech function and validate it in smartphones against gold-standard measures. Twenty different smartphones collected video data from motor tests and audio data from speech tests. Features were extracted using Google Mediapipe (movement) and Python audio analysis packages (speech). Electromagnetic sensors (60Hz) and a microphone acquired simultaneous movement and voice data, respectively. TapTalk video and audio outcomes were comparable to gold-standard data: 90.3% of video, and 98.3% of audio, data recorded tapping/speech frequencies within±1Hz of the gold-standard measures. Validation of TapTalk across a range of devices is an important step in the development of smartphone-based telemedicine and was achieved in this study. TapTalk evaluates hand motor and speechfunctions across a wide range of smartphones.Data showed 90.3% motor and 98.3% speech accuracy within +/-1Hz of gold standards.Validation advances smartphone-based telemedicine for neurodegenerative diseases.

The Kumagai Method: Feeding Techniques Using the Pigeon Baby Cleft Palate Bottle.

This study aimed to identify the P-bottle feeding techniques systematically organized by Ms. Kumagai, an expert in nursing care for children with a cleft lip and/or palate (CLP), which were developed as she gained expertise in feeding affected children. We recruited three nurses who had mastered the Kumagai method for feeding with a P-bottle. Through analysis of participants' voices and videos during interviews, we focused on aspects such as dealing with a closed mouth, inserting the nipple in cases of unilateral and bilateral CLP, dealing with the child's movements after insertion, and key considerations when squeezing the bottle. The interview analyses revealed numerous techniques used by nurses to manage the difficulties encountered while feeding children, ensuring successful provision of nourishment. Specifically, the nurses employed techniques such as placing the nipple along the midline of the child's tongue and varying the application of force on the nipple depending on the cleft type. The nurses reported that the objectives of these techniques were to prevent ulcer formation and encourage the use of the tongue, simulating original feeding movements. We explored feeding techniques and the management of associated challenges. Our results suggest that the "Kumagai Method" could be valuable in improving feeding practices.

Did the 20<SUP>th</SUP> Presidential Elections Candidates Use Populist Rhetoric? A Comparative Analysis of Declaration and Acceptance Speeches between the 19<SUP>th</SUP> and 20<SUP>th</SUP> Presidential Elections

Did the 20<SUP>th</SUP> Presidential Elections Candidates Use Populist Rhetoric? A Comparative Analysis of Declaration and Acceptance Speeches between the 19<SUP>th</SUP> and 20<SUP>th</SUP> Presidential Elections

Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration

Plain Language SummaryNeurodegenerative disorders (NDs), such as Alzheimer’s and Parkinson’s disease, are a leading cause of disability, caregiver stress, and financial strain worldwide. The number of cases, now estimated at 60 million, will triple by 2050. Early detection is crucial to improve treatments, management, and financial planning. Unfortunately, standard diagnostic methods are costly, stressful, and often hard to access due to scheduling delays and availability issues. A promising alternative consists in digital speech analysis. This affordable, noninvasive approach can identify NDs based on individuals’ voice recordings and their transcriptions. In 2023, we launched the Toolkit to Examine Lifelike Language (TELL), an online app providing robust speech biomarkers for clinical and research purposes. This paper introduces TELL v.2.0, a novel version with improved data collection, encryption, processing, storing, download, and visualization features. First, we explain the app’s basic operations and its possibilities for online and offline data collection. Second, we describe its language survey, which covers questions about demographics as well as language history, usage, competence, and difficulties. Third, we describe TELL’s speech tests, which assess key clinical features. Fourth, we outline the app’s functions for analyzing, visualizing, and downloading data. We finish by discussing the main challenges and future opportunities for TELL and the speech biomarker field. With this effort, we hope to boost the use of digital speech markers in medical and research fields.

Voice analysis in women with polycystic ovary syndrome

BackgroundPolycystic ovarian syndrome (PCOS) is a common endocrine disorder affecting one in five women of reproductive age. Polycystic appearance of the ovaries in ultrasonography is associated with oligomenorrhea, and clinical and or biochemical hyperandrogenism. PCOS has been associated with voice alterations for many years. However, there is insufficient research on voice analysis in PCOS patients.ObjectivesThis research aimed to examine the voice characteristics among women with PCOS.Patients and methodsOur study included 25 women confirmed with PCOS and 25 control. A complete voice assessment was performed for all participants using primary demographic data, vocal symptoms, auditory perceptual evaluation of the voice, Arabic version of the Voice Handicap Index, acoustic analysis, and video laryngoscopic examination of the larynx.ResultsOur study revealed statistically significant differences between PCOS and control groups in phonasthenic symptoms, auditory perceptual assessment of the voice, acoustic parameters, and laryngoscopic examination.ConclusionPCOS women with hirsutism are complaining of vocal symptoms, changes in vocal characters, and deterioration of the acoustic parameters of the voice. High serum levels of androgen can cause the deepening of the voice, which is essential for the early detection of vocal symptoms in PCOS patients to prevent virilization of their voice.Trial registrationNCT06085170. Registered 20 August 2023—retrospectively registered, https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://classic.clinicaltrials.gov/ct2/show/NCT06085170&ved=2ahUKEwjEyLqUq6KGAxWxRPEDHb57DIQQFnoECBQQAQ&usg=AOvVaw1xTurnA6mJEZ9PK0vQS8VL.

UNNIGSA: A Unified Neural Network Approach for Enhanced Stutter Detection and Gait Recognition Analysis

Stuttering, also known as stammering, is a speech disorder characterized by involuntary disruptions or disfluencies in a person's flow of speech. These disfluencies may include repetitions of sounds, syllables, or words; prolongations of sounds; and interruptions in speech known as blocks. This paper introduces Unified Neural Network for Integrated Gait and Speech Analysis (UNNIGSA), methodology that synergizes stutter detection (SD) and gait recognition through a unified neural network architecture. UNNIGSA is engineered to address two distinct yet interrelated challenges: the accurate detection of stuttering for enhanced beneficial interventions and the precise identification of individuals based on gait analysis. The system integrates a global attention mechanism to meticulously highlight salient features within speech patterns, thereby improving the accuracy of stutter classification and offering a potential leap forward in speech therapy practices. Additionally, UNNIGSA incorporates novel data processing techniques to manage the class imbalance prevalent in stuttering speech datasets, resulting in significantly enhanced performance over existing models. The methodology also extends the functionality of automatic speech recognition (ASR) systems, fostering greater inclusivity for individuals with speech disorders and enabling their more seamless interaction with virtual assistant technologies. Overall, UNNIGSA sets a new standard in the domains of speech disorder treatment and biometric identification, offering innovative solutions to long-standing challenges and paving the way for more inclusive and secure applications.

Comparison of voice quality in patients with oral cancer at both pre and post treatment stage

Background: Patients with oral cancer have very common complaints of change in voice. This research is designed to study the effect of oral cancer and its treatment on the various domains of voice like functional, physical, and emotional along with parameters of voice like pitch, loudness and quality. Method: This study included total 48 participants (34 males and 14 females) with oral cancer. The voice quality of the participants was analysed both before and after treatment using Dr. speech and the voice handicap index (VHI). First, the voice analysis done when the patient was diagnosed as a case of oral cancer by the head-neck oncosurgeon after completion of all required investigations. Patients were recalled for voice analysis after completion of treatment. Results: There was a significant difference observed in all three domains of the VHI at both before and after treatment. The difference in the functional domain was more prominent. The VHI was not linked to tumor or nodal staging. However, post treatment the voice parameters are greatly affected as compared to pre-treatment stage. Conclusions: The administration of the VHI should be made mandatory prior to treatment of oral cancer and the findings of the test should be discussed with the patient prior to start of any treatment modality and accordingly the realistic expectations can be set. Therefore, voice analysis using Dr speech will also help in timely assessment and rehabilitation of voice before and after treatment.

Pragmatic Implicature and the Articulation of Protest in Femi Osofisan’s Plays

This study was motivated by the need to explore the pragmatic strategies employed by Femi Osofisan in his plays Women of Owu, Morountodun, and Red Is the Freedom Road to depict protest and resistance. The purpose was to analyze the use of pragmatic implicature in these plays, illustrating how implied meanings and contextual cues enhance the expression of socio-political struggles. Utilizing a qualitative research design, the study conducted a content analysis of key speeches within the selected texts, examining the pragmatic implicature of the language used. Three major findings emerged: first, Osofisan’s use of implicature effectively exposed the moral hypocrisy and brutality of oppressors; second, the speeches affirmed the importance of unity and collective action in resistance movements; third, the implicit messages within the dialogues fostered a deeper emotional and ethical engagement with the audience, enhancing the plays' impact. The study concluded that pragmatic implicature is a crucial linguistic tool in Osofisan’s work, which enriches the narrative and powerfully conveys the dynamics of protest and resilience. This study contributed to a better understanding of how language can be used to articulate and inspire resistance within socio-political contexts.

A Sense of Pride and Representations: Figures of Speech Analysis on Selected Poems

This study presents an analysis of the selected poems of Restituto Acapulco, a native of Ozamiz City, Misamis Occidental. This study utilized a textual analysis focused on figures of speech and the implications of five selected poems to culture and religion. The figures of speech used in 5 poems analyzed are synecdoche, hyperbole, metaphor, personification, simile, metonymy, apostrophe, repetition, imagery, alliteration. This study has revealed the representation and meanings pertaining to the religious culture specifically faith and religious activities of the devotees and residents of Ozamiz City, Misamis Occidental, Philippines.

Computational Methods in Speech Derived Music: Field Review

Abstract Recent years have seen the emergence of diverse computational methods used to derive musical material from speech. While speech analysis, synthesis, and signal processing advancements have informed works, a review of computational methods is absent. This review addresses the gap, categorising methods from 1980 to 2023 found in literature and contemporary music festival programmes, identifying trends relating to live computation, automated compositional processes, use of acoustic-mechanical instruments, and themes including human-machine interaction, obscuring of semantic meaning, and highlighting geographical speech qualities. These approaches have broadened compositional palettes and fostered novel composer-performer-electronic interactions.

Screening for early Alzheimer's disease: enhancing diagnosis with linguistic features and biomarkers.

Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-β positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy.

In Silico Decoding of Parkinson's: Speech & Writing Analysis.

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1-4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.

Task-generated processes in second language speech production: Exploring the neural correlates of task complexity during silent pauses

Abstract The last three decades have seen significant development in understanding and describing the effects of task complexity on learner internal processes. However, researchers have primarily employed behavioral methods to investigate task-generated cognitive load. Being the first to adopt neuroimaging to study second language (L2) task effects, we aimed to provide novel insights into the neural correlates of task-related variation in L2 oral production. To advance research methodology, we also tested the utility of a neuroimaging technique, functional magnetic resonance imaging (fMRI), in examining the impact of task-related variables on L2 speech production when combined with cognitive–behavioral tools (speech analysis, expert and learner judgments). Our research focus was the effects of task complexity on silent pausing. Twenty-four Japanese learners of English completed eight simple and complex versions of decision-making tasks, half in their first language and half in their L2. The dataset for the present study included the L2 speech and fMRI data, expert judgments, and participants’ difficulty ratings of the L1 and L2 tasks they completed. Based on our findings, we concluded that brain imaging and L1 task difficulty ratings were more sensitive to detecting task complexity effects than L2 self-ratings and pausing measures. These results point to the benefits of triangulating cognitive and neural data to study task-based neurocognitive processes.

Role of pause duration in primary progressive aphasia

ABSTRACT Aims To detect differences in speech fluency in separate primary progressive aphasia syndromes (PPA) using automated analysis techniques. The resulting linguistic features are evaluated for their use in a predictive model to identify common patterns in speakers with PPA. As fluency is observable in audio recordings, its quantification may provide a low-cost instrument that augments spontaneous speech analyses in clinical practice. Methods and Procedures Speech was recorded in 14 controls, 7 nonfluent variant (nfvPPA) and 8 semantic variant (svPPA) speakers. The recordings were annotated for speech and non-speech with Kaldi, a common toolkit for speech processing software. Variables relating to fluency (pause rate, number of pauses, length of pauses) were analyzed. Outcomes and Results The best fitting distribution of pause duration was a combination of two Gaussian distributions, corresponding with pause categories short vs. long. Group level differences were found in the rate of pauses and proportion of silence: nfvPPA speakers use more short pauses relative to long pauses than control speakers, and the duration of short and long pauses is longer; svPPA speakers use more longer pauses relative to short pauses. Their short pauses are significantly shorter than those from control speakers. Participants in both PPA groups pause more frequently. SvPPA speakers are typically perceived as fluent. However, our analysis shows their fluency patterns to be distinct from control speakers, if the long-short distinction is observed. Conclusions Automatic measurements of pause duration show meaningful distinctions across the groups and might provide future aid in clinical assessment.

The effect of subthalamic nucleus deep brain stimulation on speech performance: a prospective clinical and tractography study.

Speech changes significantly impact the quality of life for Parkinson's disease (PD) patients. Deep Brain Stimulation (DBS) of the Subthalamic Nucleus (STN) is a standard treatment for advanced PD, but its effects on speech remain unclear. This study aimed to investigate the relationship between STN-DBS and speech changes in PD patients using comprehensive clinical assessments and tractography. Forty-seven PD patients underwent STN-DBS, with preoperative and 3-month postoperative assessments. Speech analyses included acoustic measurements, auditory-perceptual evaluations, and fluency-intelligibility tests. On the other hand, structures within the volume tissue activated (VTA) were identified using MRI and DTI. The clinical and demographic data and structures associated with VTA (Corticospinal tract, Internal capsule, Dentato-rubro-thalamic tract, Medial forebrain bundle, Medial lemniscus, Substantia nigra, Red nucleus) were compared with speech analyses. The majority of patients (36.2-55.4% good, 29.7-53.1% same) exhibited either improved or unchanged speech quality following STN-DBS. Only a small percentage (8.5-14.9%) experienced deterioration. Older patients and those with worsened motor symptoms postoperatively were more likely to experience negative speech changes (p < 0.05). Interestingly, stimulation of the right Substantia Nigra correlated with improved speech quality (p < 0.05). No significant relationship was found between other structures affected by VTA and speech changes. This study suggests that STN-DBS does not predominantly negatively impact speech in PD patients, with potential benefits observed, especially in younger patients. These findings underscore the importance of individualized treatment approaches and highlight the need for further long-term studies to optimize therapeutic outcomes and better understand the effects of STN-DBS on speech.

HEAR-MHE study: Automated speech analysis identifies minimal hepatic encephalopathy and may predict future overt hepatic encephalopathy.

Hepatic encephalopathy (HE) is a major cause of poor quality of life in patients with cirrhosis. A simple diagnostic test to identify minimal HE (MHE) and predict future overt HE (OHE) is lacking. We aimed to evaluate if analysis of speech patterns using a modern speech platform: 1) correlates with validated HE tests, 2) correlates with MHE, and 3) predicts future OHE. In a two-center prospective cohort study of 200 outpatients with cirrhosis and 50 controls, patients underwent baseline speech recording and validated HE diagnostic testing with psychometric HE score (PHES). Patients were followed for 6 months to identify episodes of OHE. 752 speech variables were extracted using an automated speech analysis platform, reflecting the acoustic, lexical, and semantic aspects of speech. Patients with cirrhosis were median 63 years old (IQR 54, 68), 49.5% (99) were female. Over 100 speech variables were significantly associated with PHES (p <0.05 with FDR adjustment). A three-variable speech model (two acoustic, one speech tempo variable) was similar to animal naming test in predicting MHE (AUC 0.76 vs. 0.69; p=0.11). Adding age and MELD-Na improved accuracy of the speech model (AUC: 0.82). A combined clinical-speech model ("HEAR-MHE model") predicted time to OHE with a concordance of 0.74 (p=0.06). Automated speech analysis highly correlated with validated HE tests, associated with MHE, and may predict future OHE. Future research is needed to validate this tool and to understand how it can be implemented in clinical practice.

Accuracy of Speech Sound Analysis: Comparison of an Automatic Artificial Intelligence Algorithm With Clinician Assessment.

Automatic speech analysis (ASA) and automatic speech recognition systems are increasingly being used in the treatment of speech sound disorders (SSDs). When utilized as a home practice tool or in the absence of the clinician, the ASA system has the potential to facilitate treatment gains. However, the feedback accuracy of such systems varies, a factor that may impact these gains. The current research analyzes the feedback accuracy of a novel ASA algorithm (Amplio Learning Technologies), in comparison to clinician judgments. A total of 3,584 consonant stimuli, produced by 395 American English-speaking children and adolescents with SSDs (age range: 4-18 years), were analyzed with respect to automatic classification of the ASA algorithm, clinician-ASA agreement, and interclinician agreement. Further analysis of results as related to phoneme acquisition categories (early-, middle-, and late-acquired phonemes) was conducted. Agreement between clinicians and ASA classification for sounds produced accurately was above 80% for all phonemes, with some variation based on phoneme acquisition category (early, middle, late). This variation was also noted for ASA classification into "acceptable," "unacceptable," and "unknown" (which means no determination of phoneme accuracy) categories, as well as interclinician agreement. Clinician-ASA agreement was reduced for misarticulated sounds. The initial findings of Amplio's novel algorithm are promising for its potential use within the context of home practice, as it demonstrates high feedback accuracy for correctly produced sounds. Furthermore, complexity of sound influences consistency of perception, both by clinicians and by automated platforms, indicating variable performance of the ASA algorithm across phonemes. Taken together, the ASA algorithm may be effective in facilitating speech sound practice for children with SSDs, even in the absence of the clinician.

The Trump effect: the perpetuation of populism in US–China trade

Abstract Our article analyses the enduring influence of Donald Trump's populist narrative on United States–China trade policies under Joe Biden's administration. We extend the literature on the effect of populism and populist leaders by examining what happens after a populist leader leaves office, and to what extent an expectation of populism perpetuates a populist narrative despite a change in leadership. In the case of US–China trade, the Biden administration that followed Trump's has carried on with a populist trade narrative and protectionist policies towards China, despite predictions that the US would return to ‘normal’ economic relations after Trump left office. Through textual analysis of speeches by both administrations, we identify a narrative coherence between Trump's nationalist–populist discourse and the trade rhetoric of the Biden administration, revealing a legacy of narrative expectation that constrains policy choices. Our findings demonstrate how Trump's populist framework persists, shaping White House discourse and policy decisions in the economic realm even after he has left office, and we showcase the enduring effect of populist language in political spheres.

Leadership in international populism: How Viktor Orbán’s Hungary shows the way

Although a burgeoning literature explores the international dimensions of populism, we still know little about how populist leaders gain international popularity. This paper describes how Viktor Orbán, the Prime Minister of Hungary, has emerged as the role model of international right-wing populism. It demonstrates that Orbán actively supplied himself as an example that like-minded politicians should follow. We draw on the concept of legitimation strategies to illustrate why Orbán markets himself as the leader of an international movement against liberal ideologues (Wajner, 2022). At the same time, populists in many countries have searched abroad for a successful model to follow. Orbán’s anti-migration policies and his defense of conservative family values constitute chapters of an illiberal playbook that right-wing populists are eager to implement. We use the concept of authoritarian learning to develop this demand-side perspective (Hall and Ambrosio, 2017). Empirically, we demonstrate our argument through an analysis of speeches delivered at a leading gathering of right-wing populists and moral conservatives: the Hungarian editions of the Conservative Political Action Conference (CPAC) that took place in Budapest in 2022 and 2023.