Temporal Speech Parameters Detect Mild Cognitive Impairment in Different Languages: Validation and Comparison of the Speech-GAP Test® in English and Hungarian

Abstract

Background: The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. Objective: The main goal of this international pilot study is to address the question whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. Method: After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarian-speaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen’s criteria. Speech of each participant was recorded via a spontaneous speech task. 15 temporal parameters were determined and calculated by means of ASR. Results: Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC group. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). Conclusion: The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.

Machine Learning Approaches for Dementia Detection Through Speech and Gait Analysis: A Systematic Literature Review

“…It is worth noting that most of the examined studies predominantly focused on single-language datasets, and only one study adopted a multicultural and multilingual [79] database for dementia detection from speech. Moreover, only two studies used two distinct languages [75,76] for dementia detection from speech. While these studies have undoubtedly contributed valuable insights into the application of machine learning in speech detection, the variability in the languages of the data is one of the challenges that could be explored further to build a system that can generalize to multiple languages.…”

“…Nagumo et al [63] extracted acoustic features, mainly they focus on the temporal aspect of the speech. Kálmán et al [75] used automatic speech recognition to assess fifteen temporal parameters, including speech tempo, articulation tempo, silent pause duration rate, total pause duration rate, silent pause average duration, and total pause average duration, revealing significant differences between individuals with MCI and HCs in both English and Hungarian-speaking samples.…”

“…This technology makes use of advanced machine learning algorithms and automatic speech recognition systems to convert speech into text with remarkable accuracy. In their research [52,75], they developed their own ASR.…”

HuMenDisCo: A Hungarian Speech Corpus of Schizophrenia, Schizoaffective and Bipolar Disorders

“…During the last few decades, several spoken language corpora have been created and utilized in different psycholinguistic studies in several languages (e.g. Calvo Kálmán et al, 2022). However, to the best of our knowledge, a Hungarian corpus which allows us to systematically compare the spontaneous speech of SZ, SAD, BD and controls had not been created prior to our recent research project.…”

Temporal analysis of spontaneous speech for early screening of cognitive impairment among the elderly

“…Kálmán, J., Devanand, D. P., Gosztolya, G., Balogh, R., Imre, N., Tóth, L., Hoffmann, I., Kovács, I., Vincze, V., & Pákáski, M. (2022). Temporal speech parameters detect mild cognitive impairment in different languages: validation and comparison of the Speech-GAP Test ® in English and Hungarian.…”