Examination of the sensitivity of acoustic-phonetic parameters of speech to depression

Vicsi, Klára; Schiferl, D.; Kiss, Gábor

doi:10.1109/coginfocom.2012.6422035

Cited by 23 publications

(16 citation statements)

References 16 publications

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“…(Goldstein, McGhee, 1972;Martin, 2007), and on the other hand, for its acoustic properties, e.g. (Bachorowski et al, 2001;Bickley, Hunnicutt, 1992;Rothgänger et al, 1998;Vicsi et al, 2012). The perceivable sound sequence(s) of typical laughter is usually like those of breathy CV syllables (e.g., /hV/ syllable).…”

Section: Automatic Laughter Detectionmentioning

confidence: 99%

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Gosztolya

Beke

Neuberger

et al. 2016

Archives of Acoustics

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Laughter is one of the most important paralinguistic events, and it has specific roles in human conversation. The automatic detection of laughter occurrences in human speech can aid automatic speech recognition systems as well as some paralinguistic tasks such as emotion detection. In this study we apply Deep Neural Networks (DNN) for laughter detection, as this technology is nowadays considered state-of-the-art in similar tasks like phoneme identification. We carry out our experiments using two corpora containing spontaneous speech in two languages (Hungarian and English). Also, as we find it reasonable that not all frequency regions are required for efficient laughter detection, we will perform feature selection to find the sufficient feature subset.

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Section: Automatic Laughter Detectionmentioning

confidence: 99%

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Gosztolya

Beke

Neuberger

et al. 2016

Archives of Acoustics

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“…Depressed people were hesitant when speaking, and therefore produced more frequent pauses. It was therefore shown that measurements of acoustics features, such as the number of pauses, reflected the level of depression (Vicsi et al 2012). The research findings indicated that depressed adults spoke less loudly than those who were not suffering from depression.…”

Section: Introductionmentioning

confidence: 93%

“…However, loudness measurements have not been frequently used in the research. Previous research findings have suggested that the measurements of acoustics features, such as fundamental frequency (F0) and pauses reflect the level of depression (Vicsi et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…One fourth or 25% of students at Thammasart University have experienced depression (Angkapanichkit et al 2019). It would be advantageous to be able to recognize whether youths in schools or institutions and working people are suffering from depression through analysis of the most reliable markers in daily life, particularly the fundamental frequency (F0) and silent pauses (Vicsi et al 2012). Therefore, this study aims to analyze fundamental frequency (F0) and silent pauses in depressive teenagers compared to non-depressed teenagers.…”

Section: Introductionmentioning

confidence: 99%

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An Acoustic Study of Fundamental Frequency and Pauses in Depressive Teenagers in Thailand: A Pilot Study

Teeranon¹

2020

J. Univers. Lang.

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Depression is a major public health problem among Thais. It has been reported that 30% of 70 million people living in Thailand suffer from depression. Suicide is a potential end result from mental illness, * I would like to express my gratitude to Lampang Hospital, Lampang Province Thailand and the Unit of Excellence, University of Phayao for their assistance with ethical issues (no.3/019/62) and funding. My sincere thanks go to Doctor Nutmethee Noppachaianun, Head of the Psychiatric Department, Ms. Rungravee Kaewdee, and all of his psychiatrists and nurses. In addition, I would like to thank my participants and my research team for their support.

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“…Previous research on the use of these indices in the analysis of illness has shown that patients with depression pronounce vowels with higher shimmer and jitter values. Additionally, it has been shown that the frequencies of the first and second formants of depression patient speech are lower than those of healthy individuals [9]. In other research, the Lyapunov exponents and Kolmogrov entropy of the voices of depression patients were measured using chaos analysis methods [10].…”

Section: Introductionmentioning

confidence: 99%

Multilingual evaluation of voice disability index using pitch rate

Shinohara

Omiya

Nakamura

et al. 2017

Adv. sci. technol. eng. syst. j.

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Examination of the sensitivity of acoustic-phonetic parameters of speech to depression

Cited by 23 publications

References 16 publications

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

An Acoustic Study of Fundamental Frequency and Pauses in Depressive Teenagers in Thailand: A Pilot Study

Multilingual evaluation of voice disability index using pitch rate

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