Using Psycholinguistic Features for the Classification of Comprehenders from Summary Speech Transcripts

Barnwal, Santosh Kumar; Tiwary, Uma Shanker

doi:10.1007/978-3-319-72038-8_10

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In particular, some researchers prove that the emotional component of the communication process plays an important role in its effectiveness (Wen et al 2019). The ability to manage emotions and express them depends not only on the emotional state, but also on the ability to appropriately and effectively use language tools and skills (Barnwal and Tiwary 2017). This indicates that the communicative process is a combination of the emotional component and speech abilities.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of emotional intelligence on the psycholinguistic peculiarities of speech

Zhylin,

Smokovab,

Mendelo

et al. 2023

Multidiscip. Sci. J.

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Emotional intelligence is an indicator of students’ development, which affects their psycholinguistic peculiarities and ensures effective professional training. The aim of the research is to identify the impact of the level of emotional intelligence on the psycholinguistic peculiarities of students. The survey and testing method was used, diagnostic techniques: S. L. Bratchenko’s Orientation of the Individual in Communication, V. M. Rusalov’s QFDPI, EQ-test. Descriptive statistics and correlation analysis were used to analyse the obtained results. It was established that high emotional intelligence has a positive effect on dialogic speech (r=0.832, p≤0.01) and focus on the interlocutor (r=0.835, p≤0.01). Students with developed emotional intelligence have ease in establishing communicative relationships (r=0.867, р≤0.01), a wide range of communicative programmes (r=0.745, р≤0.01), high speech activity (r=0.640, р ≤0.01), emotional stability and confidence in communication (r=0.856, p≤0.01). It was established that emotional intelligence affects the psycholinguistic features of speech. In the process of professional training, developed emotional intelligence ensures the preparation of highly qualified specialists capable of active communicative interaction. The obtained results, which reflect the regularities of the development of emotional intelligence and its influence on the psycholinguistic features of students, provide an opportunity to build professional training programmes, to develop correctional classes for the complex speech development of students by means of emotional intelligence.

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Section: Literature Reviewmentioning

confidence: 99%

The impact of emotional intelligence on the psycholinguistic peculiarities of speech

Zhylin,

Smokovab,

Mendelo

et al. 2023

Multidiscip. Sci. J.

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“…Vulnerable people such as patients with memory problems and second language speakers are expected to have a lower TTR [11]. Psycholinguistic features were used for speech transcripts summarisation [17].…”

Section: Related Workmentioning

confidence: 99%

Identifying Indicators of Vulnerability from Short Speech Segments Using Acoustic and Textual Features

Cui¹,

Gamage²,

Hanley³

et al. 2021

Interspeech 2021

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In order to protect vulnerable people in telemarketing, organisations have to investigate the speech recordings to identify them first. Typically, the investigation is manually conducted. As such, the procedure is costly and time-consuming. With an automatic vulnerability detection system, more vulnerable people can be identified and protected. A standard telephone conversation lasts around 5 minutes, the detection system is expected to be able to identify such a potential vulnerable speaker from speech segments. Due to the complexity of the vulnerability definition and the unavailable annotated vulnerability examples, this paper attempts to address the detection problem as three classification tasks: age classification, accent classification and patient/non-patient classification utilising publicly available datasets. In the proposed system, we trained three sub models using acoustic and textual features for each sub task. Each trained model was evaluated on multiple datasets and achieved competitive results compared to a strong baseline (i.e. in-dataset accuracy).

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“…Automatically detecting Alzheimer’s disease using transcripts of conversations is not new. Some previous efforts [14, 15] used linguistic features, such as PoS tags and syntactic complexity; psycholinguistic features (e.g, lexical and morphological complexity, word information, and readability etc.) to detect Alzheimer’s disease.…”

Section: Related Workmentioning

confidence: 99%

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

Wang

Chen

Subbalakshmi

2020

Preprint

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In this work we propose three explainable deep learning architectures to automatically detect patients with Alzheimer's disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional Convolutional Neural Network (CNN) to generate two types of explanations of the model's action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentations and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952 on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.

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Using Psycholinguistic Features for the Classification of Comprehenders from Summary Speech Transcripts

Cited by 3 publications

References 12 publications

The impact of emotional intelligence on the psycholinguistic peculiarities of speech

The impact of emotional intelligence on the psycholinguistic peculiarities of speech

Identifying Indicators of Vulnerability from Short Speech Segments Using Acoustic and Textual Features

Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

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