| 1. |
- Bartl-Pokorny, KD, et al.
(författare)
-
Vocalisation Repertoire at the End of the First Year of Life: An Exploratory Comparison of Rett Syndrome and Typical Development
- 2022
-
Ingår i: Journal of developmental and physical disabilities. - : Springer Science and Business Media LLC. - 1056-263X .- 1573-3580. ; 3534:16, s. 1053-1069
-
Tidskriftsartikel (refereegranskat)abstract
- Rett syndrome (RTT) is a rare, late detected developmental disorder associated with severe deficits in the speech-language domain. Despite a few reports about atypicalities in the speech-language development of infants and toddlers with RTT, a detailed analysis of the pre-linguistic vocalisation repertoire of infants with RTT is yet missing. Based on home video recordings, we analysed the vocalisations between 9 and 11 months of age of three female infants with typical RTT and compared them to three age-matched typically developing (TD) female controls. The video material of the infants had a total duration of 424 min with 1655 infant vocalisations. For each month, we (1) calculated the infants’ canonical babbling ratios with CBRUTTER, i.e., the ratio of number of utterances containing canonical syllables to total number of utterances, and (2) classified their pre-linguistic vocalisations in three non-canonical and four canonical vocalisation subtypes. All infants achieved the milestone of canonical babbling at 9 months of age according to their canonical babbling ratios, i.e. CBRUTTER ≥ 0.15. We revealed overall lower CBRsUTTER and a lower proportion of canonical pre-linguistic vocalisations consisting of well-formed sounds that could serve as parts of target-language words for the RTT group compared to the TD group. Further studies with more data from individuals with RTT are needed to study the atypicalities in the pre-linguistic vocalisation repertoire which may portend the later deficits in spoken language that are characteristic features of RTT.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Lang, S, et al.
(författare)
-
Early Detection of Developmental Disorders: The role of Canonical Babbling
- 2021
-
Ingår i: KINDHEIT UND ENTWICKLUNG. - : Hogrefe Publishing Group. - 0942-5403 .- 2190-6246. ; 30:1, s. 15-24
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Zusammenfassung. Theoretischer Hintergrund: Forschungsergebnisse zum frühen Spracherwerb bei Kindern mit spät erkannten Entwicklungsstörungen sind inkonsistent und angesichts unterschiedlicher Forschungsparadigmen und Definitionen nur bedingt vergleichbar. Fragestellung: Ziel dieser Übersichtsarbeit ist es, das Potenzial früher verbaler Fähigkeiten, vor allem des kanonischen Lallens, als Marker zur Früherkennung von Autismus-Spektrum-Störung, Rett-Syndrom und Fragilem-X-Syndrom zu beschreiben. Methode: Übersichtsarbeit über retrospektive und prospektive Studien zu frühen verbalen Fähigkeiten in der Prodromalphase dieser Störungsbilder. Ergebnisse: Einige Kinder mit spät erkannten Entwicklungsstörungen erwerben das kanonische Lallen nach dem kritischen Zeitfenster von 6 bis 10 Monaten und/oder haben oft qualitative Veränderungen in ihren Lautäußerungen. Diskussion und Schlussfolgerung: Eine abweichende Entwicklung des kanonischen Lallens kann auf spätere atypische Entwicklung hinweisen. Als funktioneller Marker für die spezifische Früherkennung von Entwicklungsstörungen scheint dieser Erwerbsmeilenstein jedoch nicht geeignet. Linguistische und signalbasierte Detailanalysen frühkindlicher Vokalisationen könnten zukünftig einen entscheidenden Beitrag hierzu leisten.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Pokorny, FB, et al.
(författare)
-
Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome
- 2022
-
Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 13345-
-
Tidskriftsartikel (refereegranskat)abstract
- Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)–(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling.
|
|
| 9. |
- Pokorny, FB, et al.
(författare)
-
Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development
- 2020
-
Ingår i: Journal of nonverbal behavior. - : Springer Science and Business Media LLC. - 0191-5886 .- 1573-3653. ; 44:4, s. 419-436
-
Tidskriftsartikel (refereegranskat)abstract
- Human preverbal development refers to the period of steadily increasing vocal capacities until the emergence of a child’s first meaningful words. Over the last decades, research has intensively focused on preverbal behavior in typical development. Preverbal vocal patterns have been phonetically classified and acoustically characterized. More recently, specific preverbal phenomena were discussed to play a role as early indicators of atypical development. Recent advancements in audio signal processing and machine learning have allowed for novel approaches in preverbal behavior analysis including automatic vocalization-based differentiation of typically and atypically developing individuals. In this paper, we give a methodological overview of current strategies for collecting and acoustically representing preverbal data for intelligent audio analysis paradigms. Efficiency in the context of data collection and data representation is discussed. Following current research trends, we set a special focus on challenges that arise when dealing with preverbal data of individuals with late detected developmental disorders, such as autism spectrum disorder or Rett syndrome.
|
|
| 10. |
|
|
