Analysis of mrate, shimmer, jitter, and F/sub 0/ contour features across stress and speaking style in the SUSAS database

Slyh, Raymond E.; Nelson, W. Todd; Hansen, Eric G.

doi:10.1109/icassp.1999.758345

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…For this reason, the interpretation of the results was made keeping in mind this aspect. Another aspect to consider was that, according to [24], significant differences could occur in jitter and shimmer measurements between different speaking styles, especially concerning the shimmer measure. Praat software was used to extract these measurements from all of our test samples [25].…”

Section: Jitter and Shimmer Parametersmentioning

confidence: 99%

Influence of TTS Systems Performance on Reaction Times in People with Aphasia

et al. 2021

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Text-to-speech (TTS) systems provide fundamental reading support for people with aphasia and reading difficulties. However, artificial voices are more difficult to process than natural voices. The current study is an extended analysis of the results of a clinical experiment investigating which, among three artificial voices and a digitised human voice, is more suitable for people with aphasia and reading impairments. Such results show that the voice synthesised with Ogmios TTS, a concatenative speech synthesis system, caused significantly slower reaction times than the other three voices used in the experiment. The present study explores whether and what voice quality metrics are linked to delayed reaction times. For this purpose, the voices were analysed using an automatic assessment of intelligibility, naturalness, and jitter and shimmer voice quality parameters. This analysis revealed that Ogmios TTS, in general, performed worse than the other voices in all parameters. These observations could explain the significantly delayed reaction times in people with aphasia and reading impairments when listening to Ogmios TTS and could open up consideration about which TTS to choose for compensative devices for these patients based on the voice analysis of these parameters.

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Section: Jitter and Shimmer Parametersmentioning

confidence: 99%

Influence of TTS Systems Performance on Reaction Times in People with Aphasia

et al. 2021

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“…Any voice signal can be converted to Mel-frequency signal using (1). 100 1 log 2595 10 f f Mel (1) As shown in Fig.1, an audio signal is passed through Preemphasis block. The purpose of this block is to compensate the high-frequency part that was suppressed during the sound production mechanism of a person.…”

Section: B Mel-frequency Cepstral Coefficientsmentioning

confidence: 99%

“…In reference [10] shimmer (dB) is expressed as the variability of the peak-to-peak amplitude in decibels, i.e. the average absolute base-10 logarithm of the difference between the amplitudes of consecutive periods, multiplied by 20 is expressed as…”

Section: E Jitter and Shimmermentioning

confidence: 99%

Early detection of Parkinson's disease through Voice

Vikas

Sharma

2014

2014 International Conference on Advances in Engineering and Technology (ICAET)

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this paper present different methods of diagnosis of Parkinson's disease through voice in early stages. The aim of this study is to provide a simple, fast, cheaper method of detection of Parkinson's disease for the patients as there is no cure for this disease and the available therapies which provide some relief to them are too much costlier for this disease. Voice has different parameters that tell meaningful variations between normal people and Parkinson's patient like pitch, jitter, formant, shimmer, Mel-frequency Cepstral Coefficient (MFCC) and glottal pulse were founded out and analyzed.

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“…They are commonly measured for long sustained vowels, and values of jitter and shimmer above a certain threshold are considered being related to pathological voices, which are usually perceived by humans as breathy, rough or hoarse voices. In [7] it was reported that significant differences can occur in jitter and shimmer measurements between different speaking styles, especially in shimmer measurement. Nevertheless, prosody is also highly-dependant on the emotion of the speaker, and prosodic features are useful in automatic recognition systems even when no emotional state is distinguished.…”

Section: Introductionmentioning

confidence: 99%

Jitter and shimmer measurements for speaker recognition

2007

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Jitter and shimmer are measures of the cycle-to-cycle variations of fundamental frequency and amplitude, respectively, which have been largely used for the description of pathological voice quality. Since they characterise some aspects concerning particular voices, it is a priori expected to find differences in the values of jitter and shimmer among speakers. In this paper, several types of jitter and shimmer measurements have been analysed. Experiments performed with the Switchboard-I conversational speech database show that jitter and shimmer measurements give excellent results in speaker verification as complementary features of spectral and prosodic parameters.

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Analysis of mrate, shimmer, jitter, and F/sub 0/ contour features across stress and speaking style in the SUSAS database

Cited by 16 publications

References 4 publications

Influence of TTS Systems Performance on Reaction Times in People with Aphasia

Influence of TTS Systems Performance on Reaction Times in People with Aphasia

Early detection of Parkinson's disease through Voice

Jitter and shimmer measurements for speaker recognition

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