Aphasia is a language disorder affecting a person's ability to communicate. It is caused by damage to the brain's language centers, typically resulting from a stroke, brain injury, or neurodegenerative conditions.
ASHA defines aphasia as an acquired neurogenic communication disorder that impairs the comprehension and production of spoken and written language and the ability to use language effectively for communication. It involves deficits in one or more language modalities, including phonetics, phonology, morphology, syntax, semantics, and pragmatics.
Aphasia can manifest in several ways depending on the specific areas of the brain affected and the severity of the impairment. It can impact both expressive language skills (such as speaking and writing) and receptive language skills (such as understanding spoken and written language). The specific symptoms and severity of aphasia can vary widely among individuals.
Treatment for aphasia typically involves a comprehensive approach that includes language therapy, communication strategies, and augmentative and alternative communication (AAC) techniques. The goal is to improve communication abilities, enhance quality of life, and help individuals with aphasia participate more fully in social, academic, and vocational activities.
Apraxia of Speech (AOS)—also referred to as acquired apraxia of speech, verbal apraxia, or childhood apraxia of speech (CAS) in the case of pediatric diagnosis—is a neurological speech disorder. Apraxia of Speech (AOS) can also manifest as a symptom of neurodegenerative conditions, particularly in the non-fluent variant of Primary Progressive Aphasia (nfvPPA). In this context, individuals with nfvPPA may display characteristics of AOS, reflecting the underlying neurodegenerative changes that impact the brain's ability to produce fluent, articulated speech.
Individuals with AOS encounter difficulties in articulating what they intend to express accurately and consistently, due to a disruption in the neural pathways responsible for planning motor actions and sequencing the intricate movements involved in speech, such as those required by the tongue, lips, and jaw. Although the individual's cognitive understanding and intent of speech can be intact, the brain struggles to correctly orchestrate the corresponding physical movements.
However, it's important to distinguish AOS from dysarthria. The latter is a distinct speech disorder resulting from non-cognitive factors, such as muscular weakness or paralysis affecting speech, rather than the planning of movement. While these conditions can coexist in some individuals, complicating the diagnostic process, they are fundamentally different in origin and manifestation.
The impact of AOS varies significantly among individuals. In milder cases, it may only affect the pronunciation of a handful of phonemes or multi-syllabic words. In contrast, severe manifestations can result in incomprehensible speech, severely inhibiting effective verbal communication. In those case, AOS necessitates the employment of alternative communication strategies or aids.
Speech, language, and communication deficits are a common symptom in several neurodegenerative conditions. These deficits are often among the first symptoms to emerge, which makes them particularly important for early detection, diagnosis, and treatment planning. Traditional neurological assessments routinely include an evaluation of these functions as they provide critical insights into the progression of neurocognitive diseases. However, traditional speech and language evaluations pose significant challenges for clinicians as they are time-consuming and require substantial resources. In this context, we argue that the use of machine learning methodologies, natural language processing (NLP), and modern artificial intelligence (AI) for Language Assessment represents a significant improvement over conventional manual assessment methods. Open Brain AI aims to help clinicians and researchers employ CLA in their everyday practices.
Computational Language Assessment (CLA) leverages these advanced technologies to accomplish three primary goals. First, it enables a comprehensive neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. This is crucial as early detection of dementia and other neurocognitive diseases is essential for timely intervention and better patient outcomes. Second, CLA facilitates the diagnosis, prognosis, and assessment of therapy efficacy in at-risk and language-impaired populations. This helps in personalizing treatment plans and monitoring progress over time. Third, it allows easier extensibility to assess patients from a wide range of languages. This is particularly important in a globalized world where patients may speak a variety of languages that are not always comprehensively covered by traditional assessment tools.
By employing advanced AI models, CLA can also provide valuable insights into the neurocognitive theory on the relationship between language symptoms and their neural bases. This can help in understanding the underlying mechanisms of language and communication deficits in neurodegenerative diseases and inform the development of more effective interventions.
Finally, CLA significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders. This is of utmost importance as communication is a key aspect of social engagement, which in turn is associated with better cognitive and emotional well-being. By improving the assessment and management of communication disorders, CLA can help elderly individuals maintain better social engagement, thereby contributing to healthier aging.In conclusion, employing machine learning methodologies, natural language processing, and modern artificial intelligence for Language Assessment offers significant advantages over conventional manual assessment methods. Computational Language Assessment (CLA) enables a comprehensive neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia, facilitates the diagnosis, prognosis, and assessment of therapy efficacy in at-risk and language-impaired populations, and allows easier extensibility to assess patients from a wide range of languages. Moreover, it can provide valuable insights into the neurocognitive theory on the relationship between language symptoms and their neural bases, thereby contributing to the development of more effective interventions. Ultimately, CLA significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, enabling them to age gracefully with social engagement.
Every year more than ten million individuals develop dementia, with almost fifty-five million worldwide now living with dementia.
Dementia is the progressive deterioration of cognitive, linguistic, and social functioning that affects the quality of life, including the physical, social, and economic conditions of individuals, their families, and society.
There are types of dementia depending on the symptoms, underlying pathology.
Language symptoms depend on the type of dementia.
Individuals with Alzheimer's Disease (AD) constitute the larger group of individuals with dementia. They are characterized by a progressive deterioration of memory, language, conversation, and ability to perform everyday activities.
Individuals with Primary Progressive Aphasia (PPA), a progressive neurological condition, are impaired in speech and language.
Individuals with PPA are grouped into three variants based on their distinct underlying neuropathology and area of brain damage.
According to current classification criteria, their characteristic neuropathology and damage patterns give rise to different discourse deficits across three variants, namely in
INDIVIDUALS WITH THE NON-FLUENT PPA VARIANT (NFVPPA)
INDIVIDUALS WITH THE SEMANTIC PPA VARIANT (SVPPA), AND
INDIVIDUALS WITH THE LOGOPENIC PPA VARIANT (LVPPA)
Individuals with Parkinson's Disease (PD) are characterized by a progressive deterioration of movement functioning, which impairs balance, speaking, language, chewing, and swallowing.
It impairs speech, language, and communication:
- Language comprehension
- Language production of spoken and written language
- Using language effectively for communication.
- It involves deficits in one or more language modalities, including phonetics, phonology, morphology, syntax, semantics, and pragmatics.
- It can also impair aspects of functional communication, e.g.,
- Emotions self-monitoring
- Theory of Mind
The neurocognitive assessment aims to evaluate individuals’ condition and provide early diagnosis, prognosis, and quantify intervention efficacy.
Computational Language Assessment (CLA) informs the clinician and the patient by quantifying the symptoms of dementia on language and enabling monitoring of disease progression over time.
CLA provides tools that can screen individuals for dementia and provide a recommendation on whether to visit a clinician.
Although there is no treatment for dementia, pharmaceutical solutions that can potentially that slow down the progression of the disease show positive results and are getting approved.
Therefore, early-stage identification, and assessment of individuals with dementia are of utmost importance to enable interventions that can delay the progression of dementia and support family planning.
American Speech-Language-Hearing Association (ASHA): www.asha.org
National Aphasia Association: www.aphasia.org/
Alzheimer's Association: https://www.alz.org
Sickness is an impediment that affects the body not what you choose to do (…) choose to follow this for all the things that happen to you: because you will realize that it impedes something else and not yourself.
Linguistic biomarkers are particular traits or characteristics in language and speech that can be quantified and utilized to identify and monitor the progression of neurological or psychiatric disorders affecting language. These biomarkers encompass a broad spectrum of features, including alterations in vocabulary, syntax, semantic content, speech rate, prosody, and other phonological, morphological, and discourse properties due to impairment. For instance, an individual with Alzheimer's disease may display a reduction in vocabulary, increased utilization of pronouns instead of proper nouns, and challenges in constructing complex sentences. These specific language alterations can act as linguistic biomarkers for Alzheimer's disease. The detection and analysis of these linguistic biomarkers are pivotal in the early diagnosis, monitoring of disease progression, and evaluation of the efficacy of interventions.
Open Brain AI facilitates access to linguistic biomarkers for language-impacting conditions. It provides a set of tools, invaluable for clinicians and researchers in eliciting linguistic biomarkers for various language-affecting conditions. Open Brain AI supports researchers and clinicians in assessing the influence of several factors, including prosodic and segmental measures (e.g., fundamental frequency, and vowel formants), pause duration, the count of pauses and filled pauses (e.g., um and hm), and phonological measures like sound deletions, insertions, transpositions, variations in speech sound quality (e.g., vowels and consonants), intonation, and voice quality impairments.
Open Brain AI employs advanced Natural Language Processing (NLP) methods and machine learning to extract novel grammatical measures and assess existing ones from the automated transcriptions of speech productions and writings. Moreover, Open Brain AI analyzes texts for lexical richness measures and assesses the significance of related measures such as utterance length, phonemes-to-word ratio, content words, function words, part of speech ratio, and morphological and semantic information about gender, person, number, and time. It can also pinpoint the syntactic role of textual constituents using automated syntactic analysis with parsers for English, Norwegian, Swedish, Greek, and other languages.
Additionally, Open Brain AI extracts semantic relationships from texts and assists researchers in scoring semantic performance, utilizing semantic distance metrics, known as word and textual embeddings, which characterize the relationships of words provided in speech productions.
Furthermore, Open Brain AI identifies the discourse properties that differentiate the speech of individuals with speech and language impairment from healthy controls by extracting information about cohesion and coherence. It can employ idea density as a cognitive decline indicator and use topic classification machine learning models to extract discourse macrostructure insights. Sentiment analysis can also be employed to interpret stance, such as the positive and negative emotions associated with pathology.
In conclusion, Open Brain AI is a comprehensive set of tools that empowers clinicians and researchers to better comprehend and analyze the speech of individuals with language impairments and healthy controls by evaluating various linguistic, syntactic, semantic, and discourse properties.
Language Assessment in the Classroom
Language assessment plays a pivotal role in classroom settings, transcending the mere identification of students with language disorders. Its importance is underscored by its ability to shed light on how students acquire language, pinpoint their areas of strength and vulnerability, and gauge the efficacy of instructional methods. By delving into these insights, educators can calibrate their teaching approaches and extend specialized support to needy students. Standard methods involve manual language assessments, including:
- Standardized Tests: These rigorous tests are meticulously crafted to evaluate a student's prowess across various language dimensions—listening comprehension, speaking, reading, and writing being prominent among them.
- Informal Assessments: Often described as adaptive, these assessments are tailored to resonate with an individual student's needs. They encompass diverse approaches like analyzing language samples, classroom observations, and conducting student interviews.
- Self-assessment: Empowering students to introspect, self-assessment encourages them to evaluate their linguistic capacities. Tools like questionnaires, checklists, and portfolios often facilitate this introspective journey.
The choice of assessment often pivots around the student's unique requirements and the overarching objectives of the assessment. For instance, while a standardized test might be paramount for a student suspected of a language disorder, an educator seeking insights into a student's linguistic learning journey might lean towards informal assessments.
The gleanings from these assessments can be instrumental in tailoring instruction. A student grappling with reading comprehension might benefit from a differently structured text presentation or additional resources. Similarly, challenges with grammar might necessitate intensified instruction on grammatical constructs.
Moreover, these assessments can spotlight students in need of heightened support. A score below the expected benchmark on a standardized test might be a conduit to special education services for a student.
Language assessment is the linchpin that connects teaching to effective learning. Educators are better equipped to bolster instruction and support by decoding students' linguistic learning patterns.
To ensure the utmost efficacy of these assessments, educators should keep in mind:
- Relevance: The assessment should resonate with the student's age and linguistic proficiency.
- Alignment: It's crucial that the assessment dovetails with the classroom's learning aspirations.
- Equitability: Every assessment should champion fairness and be devoid of biases.
- Instructional Utility: Assessment findings should be leveraged to finetune instruction and support.
Adhering to these precepts, educators can harness language assessments to gather invaluable insights into students linguistic capabilities and devise interventions tailored to bolster their linguistic journey.
Computational Language Assessment
Computational language assessment, which employs technology and algorithms to evaluate linguistic abilities, is an improvement over traditional manual assessments in educational settings. Here's a deeper dive into how computational language assessment can complement and improve manual methods:
- 1. Efficiency and Speed: One of the most immediate advantages is speed. While manual assessments might take considerable time for grading and analysis, computational tools can quickly analyze responses, making it feasible for teachers to evaluate larger groups in less time.
- 2. Consistency: Despite their best efforts, human assessors can sometimes exhibit variability in their grading. Computational assessments ensure a consistent grading criterion, removing subjectivity and potential biases.
- 3. Adaptive Testing: Some advanced computational tools adjust the difficulty of questions in real time based on the student's performance. This ensures that students are neither overwhelmed with excessively challenging questions nor bored with overly easy ones.
- 4. Immediate Feedback: Given the speed of computational assessments, students can receive immediate feedback on their performance. This allows for instant rectification of misconceptions and promotes continuous learning.
- 5. Data-Driven Insights: Beyond mere scores, computational assessments can provide deeper insights. For example, it might identify patterns like common student mistakes, areas needing reinforcement, or even predict future performance based on current trends.
- 6. Interactive and Engaging Formats: Digital platforms allow for multimedia content integration. This means assessments can include audio, video, and interactive elements, making them more engaging for students.
- 7. Accessibility: Computational tools can be tailored to cater to students with specific needs, such as text-to-speech for visually impaired students or speech recognition for students with writing difficulties.
- 8. Saving Teacher Time: Instead of spending hours on grading, teachers can redirect their efforts toward planning targeted interventions, curriculum enhancements, or one-on-one student interactions.
- 9. Storage and Record Keeping: Computational tools can automatically archive scores and track student progress over time. This longitudinal data can be invaluable for understanding student growth and areas of improvement.
- 10. Collaboration and Sharing: Computational platforms often allow for easy sharing of results, making it feasible for teachers, parents, and even students to collaboratively discuss progress and strategies.
- 11. Customization: Based on data-driven insights, computational tools can recommend personalized resources or learning pathways for individual students.
However, while there are numerous advantages to computational language assessment, it's essential to integrate them thoughtfully with traditional methods. Manual assessments offer the human touch, understanding nuances, cultural contexts, and a holistic view of a student's abilities, which might sometimes elude even the most sophisticated algorithms. Thus, a blend of both methods, leveraging the strengths of each, would be most effective in classroom settings.
More Themistocleous (2023). Open Brain AI. Computational Language Assessment on the Web.
Stuttering is a speech disorder characterized by interruptions or disfluencies in verbal expression, where individuals that stutter involuntarily repeat or prolong sounds, syllables, or words. While technically considered a symptom rather than a disease, the term "stuttering" is often used to denote both the symptom and the disorder.
Stuttering typically manifests during childhood, often between the ages of two and five. Many children go through a phase of "developmental stuttering" which is a normal part of language development and usually resolves on its own. However, for some children, stuttering persists and can become a chronic issue.These disruptions are not easily controlled and can be both audible and silent. Stuttering often comes with additional physical movements and negative emotional responses such as fear, embarrassment, or irritation. Specifically, children who stutter may experience long- and short-term consequences:
Communication Difficulties: The primary consequence of stuttering is difficulty in fluent communication. This can impact a child's ability to express their thoughts, needs, and feelings clearly.
Emotional and Psychological Impact: Children who stutter may become frustrated or embarrassed about their speech. This can lead to anxiety or decreased self-esteem, especially in social or academic situations. They may also become more reserved, choosing to refrain from speaking in certain situations to avoid stuttering.
Social Challenges: Stuttering can affect a child's social interactions. They may be misunderstood, teased, or bullied, resulting in social isolation.
Educational Impact: Stuttering can influence academic performance. Children may avoid participating in classroom discussions, reading aloud, or giving presentations, which could affect their learning and grades.
Impact on Future Life: If left unmanaged, stuttering can persist into adulthood, affecting academic, personal, and professional life. The earlier the intervention, the better the outcomes are likely to be.