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1.
Proc Conf Assoc Comput Linguist Meet ; 2020: 177-185, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33060888

RESUMO

Many clinical assessment instruments used to diagnose language impairments in children include a task in which the subject must formulate a sentence to describe an image using a specific target word. Because producing sentences in this way requires the speaker to integrate syntactic and semantic knowledge in a complex manner, responses are typically evaluated on several different dimensions of appropriateness yielding a single composite score for each response. In this paper, we present a dataset consisting of non-clinically elicited responses for three related sentence formulation tasks, and we propose an approach for automatically evaluating their appropriateness. Using neural machine translation, we generate correct-incorrect sentence pairs to serve as synthetic data in order to increase the amount and diversity of training data for our scoring model. Our scoring model uses transfer learning to facilitate automatic sentence appropriateness evaluation. We further compare custom word embeddings with pre-trained contextualized embeddings serving as features for our scoring model. We find that transfer learning improves scoring accuracy, particularly when using pre-trained contextualized embeddings.

2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 6111-6114, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019365

RESUMO

This study describes a fully automated method of expressive language assessment based on vocal responses of children to a sentence repetition task (SRT), a language test that taps into core language skills. Our proposed method automatically transcribes the vocal responses using a test-specific automatic speech recognition system. From the transcriptions, a regression model predicts the gold standard test scores provided by speech-language pathologists. Our preliminary experimental results on audio recordings of 104 children (43 with typical development and 61 with a neurodevelopmental disorder) verifies the feasibility of the proposed automatic method for predicting gold standard scores on this language test, with averaged mean absolute error of 6.52 (on a observed score range from 0 to 90 with a mean value of 49.56) between observed and predicted ratings.Clinical relevance-We describe the use of fully automatic voice-based scoring in language assessment including the clinical impact this development may have on the field of speech-language pathology. The automated test also creates a technological foundation for the computerization of a broad array of tests for voice-based language assessment.


Assuntos
Patologia da Fala e Linguagem , Voz , Criança , Humanos , Idioma , Desenvolvimento da Linguagem , Testes de Linguagem
3.
Matern Child Health J ; 24(2): 204-212, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-31828576

RESUMO

OBJECTIVES: The primary goal was to examine outcomes of Part C early intervention (EI) referrals from a high-risk infant follow-up program and factors associated with success. A secondary aim was to determine how many referred children not evaluated by EI would have likely qualified by either automatically meeting state eligibility criteria with a condition associated with "high-probability" for developmental delays or having test scores evidencing developmental delays. METHODS: Participants included 77 children referred directly to EI from a high-risk infant follow-up program. Scores on the Bayley Scales of Infant and Toddler Development-III, basic demographics, and medical variables were extracted from electronic medical records. Information regarding referral outcomes was gathered via follow-up phone calls to EI programs several months after referral. RESULTS: Results indicate 62% of EI referrals resulted in evaluation, with 69% of those evaluated being found eligible for services. Overall, 34% of referrals resulted in EI enrollment. Of those who were not evaluated, 71% were likely to have qualified based on state eligibility criteria. Follow-up phone call results indicated the majority of families not evaluated (64%) were never successfully contacted by the EI program. CONCLUSIONS: Findings from the present study illustrate the extent of challenges in connecting families with needed EI services and indicate an opportunity for improvement in EI referral processes to increase enrollment for eligible children. Improved communication between referral sources and service providers could support enrollment with detailed documentation of prior testing and explicit reasons for referral. Follow-up calls to confirm receipt of referral may also be necessary.


Assuntos
Deficiências do Desenvolvimento/terapia , Intervenção Educacional Precoce/normas , Encaminhamento e Consulta/normas , Criança , Pré-Escolar , Deficiências do Desenvolvimento/complicações , Deficiências do Desenvolvimento/psicologia , Intervenção Educacional Precoce/métodos , Intervenção Educacional Precoce/estatística & dados numéricos , Feminino , Humanos , Lactente , Recém-Nascido , Masculino , Desenvolvimento de Programas/métodos , Desenvolvimento de Programas/estatística & dados numéricos , Encaminhamento e Consulta/estatística & dados numéricos , Fatores de Risco , População Rural/estatística & dados numéricos , População Urbana/estatística & dados numéricos , Washington/epidemiologia
4.
Interspeech ; 2019: 11-15, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33088838

RESUMO

This study explores building and improving an automatic speech recognition (ASR) system for children aged 6-9 years and diagnosed with autism spectrum disorder (ASD), language impairment (LI), or both. Working with only 1.5 hours of target data in which children perform the Clinical Evaluation of Language Fundamentals Recalling Sentences task, we apply deep neural network (DNN) weight transfer techniques to adapt a large DNN model trained on the LibriSpeech corpus of adult speech. To begin, we aim to find the best proportional training rates of the DNN layers. Our best configuration yields a 29.38% word error rate (WER). Using this configuration, we explore the effects of quantity and similarity of data augmentation in transfer learning. We augment our training with portions of the OGI Kids' Corpus, adding 4.6 hours of typically developing speakers aged kindergarten through 3rd grade. We find that 2nd grade data alone - approximately the mean age of the target data - outperforms other grades and all the sets combined. Doubling the data for 1st, 2nd, and 3rd grade, we again compare each grade as well as pairs of grades. We find the combination of 1st and 2nd grade performs best at a 26.21% WER.

5.
Matern Child Health J ; 21(2): 290-296, 2017 02.
Artigo em Inglês | MEDLINE | ID: mdl-27435728

RESUMO

Objectives To investigate enrollment patterns in Part C Early Intervention (EI) for low birth weight (LBW) infants (≤2500 g). A secondary aim is to characterize LBW infants that are not enrolled in EI, but would qualify by meeting criteria for a condition associated with a "high-probability" for developmental delays (i.e., Intraventricular Hemorrhage grade III or higher, Apgar score of ≤5 at 5 min, and/or birth weight of ≤1200 g). Methods Data were gathered from 165 LBW infants participating in a high-risk infant follow-up program. Developmental assessment was completed. Basic demographic information and data regarding enrollment in EI were collected via parent questionnaire. Medical variables were extracted from each infant's electronic medical record. Results 71.5 % of LBW infants were not enrolled in EI. Factors influencing probability of EI enrollment included birth weight, gestational age, developmental test scores, and insurance status. Of the 107 infants living in Oregon who were not enrolled in EI, 42.1 % would qualify for services due to an early medical condition identified in Oregon as a condition associated with a "high-probability" for developmental delays. Conclusions Less than one third of LBW infants were enrolled in EI by their first visit to a high-risk infant follow-up program. Those infants demonstrating developmental delays and public insurance were more likely to be enrolled. The majority of infants who have readily identifiable medical risk factors that qualify them for EI were not enrolled. This study was limited by the constraints implicated by using a clinical sample.


Assuntos
Desenvolvimento Infantil , Intervenção Médica Precoce/métodos , Recém-Nascido de Baixo Peso , Cuidado Pós-Natal/métodos , Deficiências do Desenvolvimento/prevenção & controle , Intervenção Médica Precoce/normas , Feminino , Humanos , Recém-Nascido , Modelos Logísticos , Masculino , Oregon , Fatores de Risco , Cooperação e Adesão ao Tratamento
6.
Infant Behav Dev ; 31(3): 422-31, 2008 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-18289693

RESUMO

Vocal babbling involves production of rhythmic sequences of a mouth close-open alternation giving the perceptual impression of a sequence of consonant-vowel syllables. Petitto and co-workers have argued vocal babbling rhythm is the same as manual syllabic babbling rhythm, in that it has a frequency of 1 cycle per second. They also assert that adult speech and sign language display the same frequency. However, available evidence suggests that the vocal babbling frequency approximates 3 cycles per second. Both adult spoken language and sign language show higher frequencies than babbling in their respective modalities. No information is currently available on the basic rhythmic parameter of intercyclical variability in either modality. A study of reduplicative babbling by 4 infants and 4 adults producing reduplicated syllables confirms the 3 per second vocal babbling rate, as well as a faster rate in adults, and provides new information on intercyclical variability.


Assuntos
Desenvolvimento da Linguagem , Linguística/métodos , Periodicidade , Fala/fisiologia , Desenvolvimento Infantil/fisiologia , Feminino , Humanos , Lactente , Estudos Longitudinais , Masculino , Fatores de Tempo
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