Voice - How humans communicate?

Tiwari, Manjul; Tiwari, Maneesha

doi:10.4103/0976-9668.95933

Cited by 22 publications

(20 citation statements)

References 30 publications

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“…The need for a faster procedure is real respecting the limitation of human and technical resources made available to the authorities 20 . This study shows that the choice for textualization significantly save time for audio treatments 29,30 , i.e., with the time factor being significant, the judiciary power should opt for textualization.…”

Section: Resultsmentioning

confidence: 99%

Coincidências e divergências entre transcrição e textualização de áudios

Rehder

Cazumbá

Assis³

et al. 2014

Rev. CEFAC

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Purpose: investigate coincidences and divergences between audio transcription and textualization in order to check for statistical evidence which may be a justification as to the best procedure to be applied. Methods: retrospective study. 30 audios were selected randomly among the 239 available audios, proceeding from telephone intercepts of the same lawsuit. We considered: the number of words and time in minutes for each audio, and the comparative analysis for maintaining the main content highlights. Three Speech Pathologists transcribed and textualized different parts of the file, ensuring independence. A Speech Pathologists, who did not attend the previous step, conducted content analysis. For statistical analysis we used Wilcoxon-Mann Whitney test in R environment, with Tinn R interface. Significance level 5% (0.05). CEP: 274-742. Results: the mean number of words used in of audio file transcription was 27% greater than the number of words used in textualization, p=0.52. The mean time in minutes required to perform the transcription was significantly higher p=0.013. In the comparative analysis as for the maintenance of the main content highlights, we found that on average 93% of highlights were kept, p=0.61%. Conclusion: the similarities among the of transcription and textualization processes were compared with the median number of words and the maintenance as for the median number of content highlights. There was divergence as for the implementation time, significantly lower in textualization. Considering the data obtained in this study, textualization process proved to be the most suitable in audio de-recording.

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Section: Resultsmentioning

confidence: 99%

Coincidências e divergências entre transcrição e textualização de áudios

Rehder

Cazumbá

Assis³

et al. 2014

Rev. CEFAC

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“…Its efficacy is attributed to the anatomical structure of the speech organ, which imposes certain restriction on its use, as well as to articulation habits, which are largely conditioned by the speaker's environment even when subjected to conscious control [4][5][6]. Research attempting to assess the validity of speaker identification based on vowel formants distinguishes three ways to measure frequencies.…”

Section: Introductionmentioning

confidence: 98%

Speaker identification based on artificial neural networks. Case study: the Polish vowel (pilot study)

Sałapa

Trawińska²,

Roterman

et al. 2014

Bio-Algorithms and Med-Systems

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Current statistical methods and technologies used for speaker identification via dynamic formant frequency often involve classic multivariate analyses that must meet a number of criteria in order to be considered trustworthy. The authors propose more advanced classification techniques, including artificial neural networks. Owing to iterative learning algorithms, neural networks can be trained to detect highly complex, nonlinear relations hidden in input data. This study specifically considers feed-forward multilayer perceptron and radial basic function network models. The investigation involves an analysis of the Polish vowel ɑ (stressed or unstressed) in selected contexts described by the four lowest formant frequencies. Results indicate high accuracy of neural networks as a speaker identification tool reaching up to 100%. In addition, the authors have determined that the accuracy of classification is similar when based on a single context to when input data are aggregated over several different contexts.

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“…In addition, of course, besides facial expressions, voice also conveys emotions. 22,23 Recognizing emotions in voice is also considered to be crucial for communicating with others.…”

Section: Introductionmentioning

confidence: 99%

Modafinil Decreased Thalamic Activation in Auditory Emotional Processing: A Randomized Controlled Functional Magnetic Resonance Imaging Study

et al. 2021

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Background: Modafinil is effective for improving wakefulness and attention, is used to treat patients with narcolepsy as an approved drug in Japan, and has been reported to be effective in attention-deficit hyperactivity disorder. On the other hand, modafinil has been reported to induce emotional instability including mania, depression, and suicidal ideation. Such side effects may be related to changes in cognitive behavior by the influence of modafinil on cerebral function of emotional recognition. However, the effect of modafinil on the neural basis of emotional processing has not been fully verified. We aimed to clarify the effects of modafinil on the neural basis of auditory emotional processing by functional magnetic resonance imaging. Methods: This study adopted a placebo-controlled within-subject cross-over design. The data of 14 subjects were finally analyzed. The effect of modafinil on cerebral activation and task performance during an emotional judgement task was analyzed. Results: Accuracy of the task decreased significantly and response time of emotional judgement was significantly delayed by modafinil compared with placebo. Right thalamic activation in auditory emotional processing was significantly less in the modafinil group than the placebo group. In addition, reduction of right thalamic activation by modafinil was positively correlated with the decrease in accuracy of emotional judgement. Conclusions: Our findings suggest that modafinil could act on the right thalamus, changing the behavior and brain function associated with auditory emotional processing. These results indicate that modafinil could change emotional recognition by reducing emotional activation related to social communication.

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Voice - How humans communicate?

Cited by 22 publications

References 30 publications

Coincidências e divergências entre transcrição e textualização de áudios

Coincidências e divergências entre transcrição e textualização de áudios

Speaker identification based on artificial neural networks. Case study: the Polish vowel (pilot study)

Modafinil Decreased Thalamic Activation in Auditory Emotional Processing: A Randomized Controlled Functional Magnetic Resonance Imaging Study

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