The role of frequency in the retrieval of nouns and verbs in aphasia

Background: Word retrieval in aphasia involves different levels of processing; lemma retrieval, grammatical encoding, lexeme retrieval and phonological encoding, before articulation can be programmed and executed. Several grammatical, semantic, lexical and phonological characteristics, such as word class, age of acquisition, imageability and word frequency influence the degree of success in word retrieval. It is, however, not yet clear how these factors interact. The current study focuses on retrieval of nouns and verbs in isolation and in sentence context and evaluates the impact of the mentioned factors on the performance of a group of 54 aphasic individuals. Aims: The main aim is to measure the effect of word frequency on the retrieval of nouns and verb by disentangling the influence of word class, age of acquisition, imageability and lemma and lexeme frequency on word retrieval in aphasia. Outcomes and Results: Word class, age of acquisition and imageability play a significant role in the retrieval of nouns and verbs: nouns are easier than verbs; the earlier a word has been learned and the more concrete it is, the easier it is to retrieve. When performance is controlled for these factors, lemma frequency turns out to play a minor role: only in object naming it affects word retrieval: the higher the lemma frequency of a noun, the easier it is to access. Such an effect does not exist for verbs, neither on an action-naming test, nor when verbs have to be retrieved in sentence context. Lexeme frequency was not found to be a better predictor than lemma frequency in predicting word retrieval in aphasia. Conclusions: Word retrieval in aphasia is influenced by grammatical, semantic and lexical factors. Word frequency only plays a minor role: it affects the retrieval of nouns, but not of verbs

Classification of Spontaneous Speech of Individuals with Dementia Based on Automatic Prosody Analysis Using Support Vector Machines (SVM)

Analysis of spontaneous speech is an important tool for clinical linguists to diagnose various types of neurodegenerative disease that affect the language processing areas. Prosody, fluency and voice quality may be affected in individuals with Parkinson's disease (PD, degradation of voice quality, unstable pitch), Alzheimer's disease (AD, monotonic pitch), and the non-fluent type of Primary Progressive Aphasia (PPA-NF, hesitant, non-fluent speech). In this study, the performance of a SVM classifier is evaluated that is trained on acoustic features only. The goal is to distinguish different types of brain damage based on recorded speech. Results show that the classifier can distinguish some dementia types (PPA-NF, AD), but not others (PD).<br/