Speech characteristics of childhood apraxia of speech: A survey research

PURPOSE

There has not been any valid method for the clinical diagnosis of Childhood Apraxia of Speech (CAS) up to now, and the golden standard for diagnosis is the expert's opinion. The current research was conducted to obtain criteria used by the Iranian Speech-Language Pathologists (SLPs) to establish speech characteristics and co-occurring problems of CAS based on their knowledge and clinical experience.

METHODS

This research used a questionnaire-based survey design for data collection. The questionnaire was filled out by 260 anonymous participants, both physically and online.

RESULTS

The nine top speech characteristics reported with 75.6% agreement as the core symptoms of CAS included: inconsistency (86.9%), consonant sequencing problems (75%), low intelligibility (75%), groping (72.7%), slow diadochokinetic (DDK) (72.3%), articulatory configuration problems (66.2%), difficulty with multisyllabic words (62.7%), suprasegmental disturbances (56.2%) and metathesis (53.5%). The consent of the Iranian practicing SLPs was consistent with the American Speech-Language-Hearing Association (ASHA) diagnostic criteria, Strand's 10-point checklist, and Ozanne's cluster model. More than half of the respondents have been identified with fine motor deficit and language impairment as the most common co-occurring problems of CAS.

CONCLUSIONS

The results of this study are in accordance with the findings of previous practicing SLPs' surveys in different languages. Carrying out researches on the speech characteristics of Persian-speaking children suspected of CAS and compare with the results of clinicians' surveys will help us to find a reliable standard for differential diagnosis of Persian-speaking children in Iran.

Predicting mild cognitive impairment from spontaneous spoken utterances

INTRODUCTION

Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial.

METHODS

Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants.

RESULTS

MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%.

DISCUSSION

Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.