Ildikó Hoffmann

Temporal parameters of spontaneous speech in Alzheimer's disease

This paper reports on four temporal parameters of spontaneous speech in three stages of Alzheimers disease (mild, moderate, and severe) compared to age-matched normal controls. The analysis of the time course of speech has been shown to be a particularly sensitive neuropsychological method to investigate cognitive processes such as speech planning and production. The following parameters of speech were measured in Hungarian native-speakers with Alzheimers disease and normal controls: articulation rate, speech tempo, hesitation ratio, and rate of grammatical errors. Results revealed significant differences in most of these speech parameters among the three Alzheimers disease groups. Additionally, the clearest difference between the normal control group and the mild Alzheimers disease group involved the hesitation ratio, which was significantly higher in the latter group. This parameter of speech may have diagnostic value for mild-stage Alzheimers disease and therefore could be a useful aid in medical practice.

Speaking in Alzheimer’s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer’s Disease

It is known that Alzheimer’s disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.

The perinatal outcome of pregnancy without prenatal care: A retrospective study in Szeged, Hungary

OBJECTIVE The aim of this study was to examine the social conditions of women who never attended prenatal care and to evaluate the perinatal outcome of their newborns. STUDY DESIGN A retrospective analysis of uncared pregnancies of women who delivered at the Department of Obstetrics and Gynaecology, University of Szeged, Hungary between 1 January 1996 and 31 December 1998. There were 5262 deliveries during this period, of which 54 (1%) had no prenatal care. Matched controls (108 cases) were selected on the basis of maternal age, educational level, the number of gravidity and parity, and marital status. RESULTS The mean age of women with out-of-care pregnancies was 27 years+/-3.9; 5 women were under 18, 23 (43%) were unmarried, 5 (9.3%) did not finish elementary school and 35 (65%) had only elementary school education. Compared to the controls there were more in preterm labors (33 versus 14% (OR 3.1, 95% CI 1.4-6.8)), lower birth weight (P<0.001) and more given up for adoption (17 versus 0.9% (OR 21.4, 95% CI 2.63-173.9)). CONCLUSION These data underline the importance of regular prenatal care in the prevention of preterm delivery.

The role of the putamen in cognitive functions — A case study

Abstract The role of the basal ganglia in cognition is still uncertain. This case study investigates the partial neuropsychological profile of a 20-year-old patient with a perinatal left putaminal lesion. This pathology is relatively rare and little is known of its cognitive effects. The focuses of our neuropsychological assessment were working memory, executive functions, analysis of spontaneous speech and implicit skill learning. The patients executive functions did not attain the normal range, and working memory was also partially impaired. In addition, the temporal features of her speech revealed an increased pause/signal time ratio. Finally, in an implicit skill learning task, the patient showed general motor skill learning, but no sequence specific learning. Together these findings suggest that the frontal/subcortical circuit between the putamen and frontal motor areas plays a role in higher cognitive processing such as executive functions, working memory, as well as in first-order sequence learning.

Cognitive Functions in Ataxia with Oculomotor Apraxia Type 2

Background: Ataxia with oculomotor apraxia type 2 (AOA2) is characterized by cerebellar atrophy, peripheral neuropathy, oculomotor apraxia, and elevated serum alpha-fetoprotein (AFP) levels. The disease is caused by a recessive mutation in the senataxin gene. Since it is a very rare cerebellar disorder, no detailed examination of cognitive functions in AOA2 has been published to date. The aim of the present study was to investigate the neuropsychological profile of a 54-year-old patient with AOA2. Methods: A broad range of neuropsychological examination protocol was administered including the following domains: short-term, working- and episodic-memories, executive functions, implicit sequence learning, and the temporal parameters of speech. Results: The performance on the Listening Span, Letter Fluency, Serial Reaction Time Task, and pause ratio in speech was 2 or more standard deviations (SD) lower compared to controls, and 1 SD lower on Backward Digit Span, Semantic Fluency, articulation rate, and speech tempo. Conclusion: These findings indicate that the pathogenesis of the cerebrocerebellar circuit in AOA2 is responsible for the weaker coordination of complex cognitive functions such as working memory, executive functions, speech, and sequence learning.

Detecting mild cognitive impairment from spontaneous speech by correlation-based phonetic feature selection

Mild Cognitive Impairment (MCI), sometimes regarded as a prodromal stage of Alzheimer’s disease, is a mental disorder that is difficult to diagnose. Recent studies reported that MCI causes slight changes in the speech of the patient. Our previous studies showed that MCI can be efficiently classified by machine learning methods such as Support-Vector Machines and Random Forest, using features describing the amount of pause in the spontaneous speech of the subject. Furthermore, as hesitation is the most important indicator of MCI, we took special care when handling filled pauses, which usually correspond to hesitation. In contrast to our previous studies which employed manually constructed feature sets, we now employ (automatic) correlation-based feature selection methods to find the relevant feature subset for MCI classification. By analyzing the selected feature subsets we also show that features related to filled pauses are useful for MCI detection from speech samples.

A speech recognition-based solution for the automatic detection of mild cognitive impairment from spontaneous speech

Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer’s disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive de-cline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech sig-nals, first manually (using the Praat software), and then automatically, with an automatic speech recogni-tion (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, auto-matic detection-based tool for screening MCI for the community.

Detecting mild cognitive impairment by exploiting linguistic information from transcripts

Here we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) based on linguistic features collected from their speech transcripts. Our system uses machine learning techniques and is based on several linguistic features like characteristics of spontaneous speech as well as features exploiting morphological and syntactic parsing. Our results suggest that it is primarily morphological and speechbased features that help distinguish MCI patients from healthy controls.

Identifying Mild Cognitive Impairment and mild Alzheimer’s disease based on spontaneous speech using ASR and linguistic features

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder that develops for years before clinical manifestation, while mild cognitive impairment is clinically considered as a prodromal stage of AD. For both types of neurodegenerative disorders, early diagnosis is crucial for the timely treatment and to decelerate progression. Unfortunately, the current diagnostic solutions are time-consuming. Here, we seek to exploit the observation that these illnesses frequently disturb the mental and linguistic functions, which might be detected from the spontaneous speech produced by the patient. First, we present an automatic speech recognition based procedure for the extraction of a special set of acoustic features. Second, we present a linguistic feature set that is extracted from the transcripts of the same speech signals. The usefulness of the two feature sets is evaluated via machine learning experiments, where our goal is not only to differentiate between the patients and the healthy control group, but also to tell apart Alzheimer’s patients from those with mild cognitive impairment. Our results show that based on only the acoustic features, we are able to separate the various groups with accuracy scores between 74–82%. We attained similar accuracy scores when using only the linguistic features. With the combination of the two types of features, the accuracy scores rise to between 80–86%, and the corresponding F1 values also fall between 78–86%. We hope that with the full automation of the processing chain, our method can serve as the basis of an automatic screening test in the future.