OBJECTIVE: To describe the effects of subthalamic nucleus deep brain stimulation (STN-DBS) on the speech of Spanish-speaking Parkinson\'s disease (PD) patients during the first year of treatment.
METHODS: The speech measures (SMs): maximum phonation time, acoustic voice measures, speech rate, speech intelligibility measures, and oral diadochokinesis rates of nine Colombian idiopathic PD patients (four females and five males; age = 63 ± 7 years; years of PD = 10 ± 7 years; UPDRS-III = 57 ± 6; H&Y = 2 ± 0.3) were studied in OFF and ON medication states before and every three months during the first year after STN-DBS surgery. Praat software and healthy native listeners\' ratings were used for speech analysis. Statistical analysis tried to find significant differences in the SMs during follow-up (Friedman test) and between medication states (Wilcoxon paired test). Also, a pre-surgery variation interval (PSVI) of reference for every participant and SM was calculated to make an individual analysis of post-surgery variation.
RESULTS: Non-significative post-surgery or medication state-related differences in the SMs were found. Nevertheless, individually, based on PSVIs, the SMs exhibited: no variation, inconsistent or consistent variation during post-surgery follow-up in different combinations, depending on the medication state.
CONCLUSIONS: As a group, participants did not have a shared post-surgery pattern of change in any SM. Instead, based on PSVIs, the SMs varied differently in every participant, which suggests that in Spanish-speaking PD patients, the effects of STN-DBS on speech during the first year of treatment could be highly variable.

OBJECTIVE: To analyze the clinical and genetic characteristics of a child featuring Dias-Logan syndrome.
METHODS: A child with speech disorders and delayed psychomotor development from childhood who was admitted to the Rehabilitation Medicine Department of Children\'s Hospital Affiliated to Zhengzhou University in July 2022 was selected as the research subject. Clinical data of the child was collected. Genomic DNA was extracted from peripheral blood samples of the child and his parents. Potential variant was screened by whole exome sequencing, and candidate variant was verified by Sanger sequencing.
RESULTS: The child has presented with global developmental delay, microcephaly, special facial features and behavioral problems. Genetic testing revealed a de novo variant of the BCL11A gene, namely c.561_567delACACGCA (p.Q187fs*7), which was classified as pathogenic (PVS1+PS2+PM2_Supporting).
CONCLUSIONS: The heterozygous variant of BCL11A gene probably underlay the Dias-Logan syndrome in this child. Above finding has enriched the phenotypic and mutational spectrum of the BCL11A gene and provides a basis for genetic counseling and clinical decision-making.

Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)-the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results of assessing the usefulness of hybrid feature vectors extracted based on multimodal (video and audio) data for the place of articulation assessment in sibilants /s/ and /ʂ/. We used acoustic features and, new in this field, visual parameters describing selected articulators\' texture and shape. Analysis using statistical tests indicated the differences between various sibilant realizations in the context of the articulation pattern assessment using hybrid feature vectors. In sound /s/, 35 variables differentiated dental and interdental pronunciation, and 24 were visual (textural and shape). For sibilant /ʂ/, we found 49 statistically significant variables whose distributions differed between speaker groups (alveolar, dental, and postalveolar articulation), and the dominant feature type was noise-band acoustic. Our study suggests hybridizing the acoustic description with video processing provides richer diagnostic information.

OBJECTIVE: Nasal rustle (also called nasal turbulence) refers to a loud distracting sound that sometimes occurs with audible nasal emission (ANE) during the production of pressure-sensitive consonants in patients with velopharyngeal insufficiency (VPI). This article examines evidence for two hypotheses of causality: vibration of the soft palate (velar flutter) and periodic motion of mucus above the velopharyngeal port (turbulent mucus).
CONCLUSIONS: A review of the relevant literature shows inconclusive evidence to support velar flutter as a cause of nasal rustle. In contrast, clinical observations and research involving high-speed nasopharyngoscopy suggest that nasal rustle is the result of turbulent mucus above a small velopharyngeal opening. Therefore, it is our contention that a plausible explanation for nasal rustle is one of turbulent mucus and not velar flutter.

OBJECTIVE: To compare the effect of clear twin block (CTB) and traditional twin block (TTB) appliances on speech.
METHODS: In this randomized clinical trial, 18 skeletal Class II (Class II, division 1) growing patients were selected and randomly divided into CTB and TTB groups. Objective and semiobjective speech assessment tests were performed for vowel and consonant analyses at four time intervals: before (T0), immediately after (T1), 1 month after (T2), and 3 months after (T3) inserting the appliance. Data were analyzed using analysis of variance and independent t-test at the .05 significance level.
RESULTS: Intergroup comparisons showed that the CTB group had less speech distortion immediately after insertion of the appliance (P < .05) than the TTB group; however, the differences were not significant at other time intervals. Intragroup comparisons showed that the number of distortions decreased significantly from T1 to T3 in both groups (P < .05). In contrast to CTB, the T0-T3 comparison was significant in the TTB group.
CONCLUSIONS: Although both appliances had some effects on speech, CTB had less speech distortion immediately after insertion, which is a very crucial moment in patient compliance. Additionally, articulation structures adapted to CTB faster than to TTB.

In this study, a computer-driven, phoneme-agnostic method was explored for assessing speech disorders (SDs) in children, bypassing traditional labor-intensive phonetic transcription. Using the SpeechMark® automatic syllabic cluster (SC) analysis, which detects sequences of acoustic features that characterize well-formed syllables, 1952 American English utterances of 60 preschoolers were analyzed [16 with speech disorder present (SD-P) and 44 with speech disorder not present (SD-NP)] from two dialectal areas. A four-factor regression analysis evaluated the robustness of seven automated measures produced by SpeechMark® and their interactions. SCs significantly predicted SD status (p < 0.001). A secondary analysis using a generalized linear model with a negative binomial distribution evaluated the number of SCs produced by the groups. Results highlighted that children with SD-P produced fewer well-formed clusters [incidence rate ratio (IRR) = 0.8116, p ≤ 0.0137]. The interaction between speech group and age indicated that the effect of age on syllable count was more pronounced in children with SD-P (IRR = 1.0451, p = 0.0251), suggesting that even small changes in age can have a significant effect on SCs. In conclusion, speech status significantly influences the degree to which preschool children produce acoustically well-formed SCs, suggesting the potential for SCs to be speech biomarkers for SD in preschoolers.

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BACKGROUND: The detection and characterization of speech changes may help in the identification of neurodegenerative diseases and have the potential to help with patient characterization and monitoring. Yet, there is limited research validating the presence of speech changes across different types of neurodegenerative disease. We report on the relationships between speech and other clinical assessments in the individuals with different dementia diagnoses and in comparison to healthy older adults.
METHODS: We analyzed speech recordings from 109 patients (52F, 57M; Age = 72.63± 8.61) who were diagnosed with various neurodegenerative diseases, including Alzheimer\'s disease, Frontotemporal Dementia, and Vascular Cognitive Impairment, in a cognitive neurology memory clinic. Speech recordings of an open-ended picture description task were processed using the Winterlight speech analysis platform which generates >500 acoustic and linguistic features. We investigated the linear relationship between the speech features and clinical assessments including the Mini Mental State Examination (MMSE), Western Aphasia Battery (WAB), and Mattis Dementia Rating Scale while controlling for age, sex and years of education. Speech features that were significantly associated with clinical measures were then included in group comparisons with healthy older adults (N = 74, ∼39F; Age = 61.31±7.29).
RESULTS: Speech features including lexical and syntactic features were significantly correlated with clinical assessments in patients, across diagnoses. Lower MMSE scores were associated with the use of more familiar nouns (β = -1.60, p<.001). Similarly, increased impairment as assessed by the WAB was correlated with the use of higher frequency nouns (β = -0.01, p<.001). Patients used significantly more nouns (z = 6.25, p<.001) and shorter words (z = 8.33, p<.001) than the healthy older adults. Their speech duration was also significantly shorter (z = 7.98, p<.001) and they paused more (z = 5.19, p<.001).
CONCLUSIONS: Speech changes representing decreased speech, with simpler vocabularies and syntax, were detectable in patients with different neurodegenerative diseases and correlated with clinical outcomes. These same speech patterns differed in patients with neurodegenerative disease compared to healthy older adults. Speech has the potential to be a sensitive measure for detecting cognitive impairments across various neurodegenerative diseases.

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)-electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids.

OBJECTIVE: The present investigation examined how factors such as cleft type, age of primary palatal surgery, diagnosed syndromes, hearing problems, and malocclusions could predict persistent speech difficulties and the need for speech services in school-aged children with cleft palate.
METHODS: Participants included 100 school-aged children with cleft palate. Americleft speech protocol was used to assess the perceptual aspects of speech production. The logistic regression was performed to evaluate the impact of independent variables (IV) on the dependent variables (DV): intelligibility, posterior oral CSCs, audible nasal emission, hypernasality, anterior oral CSCs, and speech therapy required.
RESULTS: Sixty-five percent of the children were enrolled in (or had received) speech therapy. The logistic regression model shows a good fit to the data for the need for speech therapy (Hosmer and Lemeshow\'s χ2(8)=9.647,p=.291). No IVs were found to have a significant impact on the need for speech therapy. A diagnosed syndrome was associated with poorer intelligibility (Pulkstenis-Robinson\'s χ2(11)=7.120,p=.789). Children with diagnosed syndromes have about six times the odds of a higher hypernasality rating (Odds Ratio = 5.703) than others. The cleft type was significantly associated with audible nasal emission (Fisher\'sexactp=.006). At the same time, malocclusion had a significant association with anterior oral CSCs (Fisher\'sexactp=.005).
CONCLUSIONS: According to the latest data in the Cleft Registry and Audit Network Annual Report for the UK, the majority of children with cleft palate attain typical speech by age five. However, it is crucial to delve into the factors that may influence the continuation of speech disorders beyond this age. This understanding is vital for formulating intervention strategies aimed at mitigating the long-term effects of speech disorders as individuals grow older.