This technical report presents a natural language processing (NLP)-based approach for systematically classifying scientific literature on childhood speech disorders. We retrieved and filtered 4,804 relevant articles published after 2015 from the PubMed database using domain-specific keywords. After cleaning and pre-processing the abstracts, we applied two topic modeling techniques - Latent Dirichlet Allocation (LDA) and BERTopic - to identify latent thematic structures in the corpus. Our models uncovered 14 clinically meaningful clusters, such as infantile hyperactivity and abnormal epileptic behavior. To improve relevance and precision, we incorporated a custom stop word list tailored to speech pathology. Evaluation results showed that the LDA model achieved a coherence score of 0.42 and a perplexity of -7.5, indicating strong topic coherence and predictive performance. The BERTopic model exhibited a low proportion of outlier topics (less than 20%), demonstrating its capacity to classify heterogeneous literature effectively. These results provide a foundation for automating literature reviews in speech-language pathology.

Individuals with language disorders often face significant communication challenges due to their limited language processing and comprehension abilities, which also affect their interactions with voice-assisted systems that mostly rely on Automatic Speech Recognition (ASR). Despite advancements in ASR that address disfluencies, there has been little attention on integrating non-verbal communication methods, such as gestures, which individuals with language disorders substantially rely on to supplement their communication. Recognizing the need to interpret the latent meanings of visual information not captured by speech alone, we propose a gesture-aware ASR system utilizing a multimodal large language model with zero-shot learning for individuals with speech impairments. Our experiment results and analyses show that including gesture information significantly enhances semantic understanding. This study can help develop effective communication technologies, specifically designed to meet the unique needs of individuals with language impairments.

Dementia is a neuro-degenerative disease affecting Early work in NLP for dementia relied on manual millions worldwide and is associated with cognitive engineered features based on specific lexical, decline, including language impairment (Forbes-acoustic and syntactic features stemming from description McKay and Venneri, 2005). Language dysfunction tasks (such as CTP), to detect linguistic may be difficult to detect in the early stages of dementia signs of cognitive decline (Fraser et al., 2016; Beltrami (Nestor et al., 2004); however, as the disease et al., 2018; Yeung et al., 2021). Recent progresses, a gradual decline of semantic knowledge work uses naive neural approaches to classify and ensues, and eventually, all linguistic functions analyse linguistic and acoustic characteristics so can be lost (Tang-Wai and Graham, 2008; Klimova as to either predict cognitive scores or achieve binary et al., 2015). Recognizing language disorders as classification of participants (Alzheimer's Disease prodromal symptoms in people with dementia may (AD) vs non-AD) (Karlekar et al., 2018; Balagopalan help with earlier diagnosis and improve disease et al., 2020; Nasreen et al., 2021b; Rohanian management.

A robot servant that can be controlled using the power of thought has been developed by MIT engineers. The machine, named Baxter, reads human brainwaves in real-time so that it knows when a human is unhappy with its actions. If a human think a mistake has been made, Baxter takes notice - and corrects himself. Baxter's owner can then make subtle hand gestures to direct the machine into performing a different task. Scientists say the technology is designed to make robots acts like an extension of a person's will, without any training.

Screening for language disorders is best done early and often, but it's not always easy to get the equipment and staff to every kid in a timely fashion. At least a basic level of screening, however, may soon be able to be automated or done at home, if research out of MIT proves reliable. Computer scientists from the school discussed a new technique at the Interspeech conference in San Francisco; it's still very early in development, but it's more than a little promising. The system created by grad student Jen Gong and professor John Guttag uses recordings of many such performances as data for a machine learning system. By closely analyzing this dataset, it learns what patterns are associated with typical development and which suggest a nascent speech or language disorder -- patterns corroborated by previous research, it bears mentioning. It's not a replacement for a trained professional, but then again, a trained professional can't be packed into an app.

For children with speech and language disorders, early-childhood intervention can make a great difference in their later academic and social success. But many such children--one study estimates 60 percent--go undiagnosed until kindergarten or even later. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital's Institute of Health Professions hope to change that, with a computer system that can automatically screen young children for speech and language disorders and, potentially, even provide specific diagnoses. This week, at the Interspeech conference on speech processing, the researchers reported on an initial set of experiments with their system, which yielded promising results. "We're nowhere near finished with this work," says John Guttag, the Dugald C. Jackson Professor in Electrical Engineering and senior author on the new paper.

Practicing the scientific approach to the data exploration one should know at what extent certain method can be applied. Neural Nets are futile for the stock market's predictions. Monte-Carlo algorithms couldn't offer much help either, and poorly implemented Random Forest algorithm can literally ruin your vacation in South-East Asia, especially if it was implemented by NSA. In this article we will briefly introduce machine learning methods classification and see how they are relevant to the different lines of business. From the cradle to the grave, we are making decisions - from our first decision to attract mother's attention to one of our last decisions when asking the doctor for pain treatment.

Practicing the scientific approach to the data exploration one should know at what extent certain method can be applied. Neural Nets are futile for the stock market's predictions. Monte-Carlo algorithms couldn't offer much help either, and poorly implemented Random Forest algorithm can literally ruin your vacation in South-East Asia, especially if it was implemented by NSA. In this article we will briefly introduce machine learning methods classification and see how they are relevant to the different lines of business. From the cradle to the grave, we are making decisions - from our first decision to attract mother's attention to one of our last decisions when asking the doctor for pain treatment.