Analysis of fundamental frequency, jitter, shimmer and vocal intensity in children with phonological disorders

Wertzner, Haydée Fiszbein; Schreiber, Solange; Amaro, Luciana

doi:10.1016/s1808-8694(15)31261-1

Cited by 51 publications

(49 citation statements)

References 13 publications

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“…Jitter refers to the variability of F0, and it is affected mainly due to the lack of control of vocal fold vibration [30,31]. On the other hand, vocal intensity is related with subglottic pressure of the air column, which, in turn, depends on other factors such as amplitude of vibration and tension of vocal folds [32].…”

Section: Jitter and Shimmermentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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Jitter and shimmer are measures of the fundamental frequency and amplitude cycle-to-cycle variations, respectively. Both features have been largely used for the description of pathological voices, and since they characterise some aspects concerning particular voices, they are expected to have a certain degree of speaker specificity. In the current work, jitter and shimmer are successfully used in a speaker verification experiment. Moreover, both measures are combined with spectral and prosodic features using several types of normalisation and fusion techniques in order to obtain better verification results. The overall speaker verification system is also improved by using histogram equalisation as a normalisation technique previous to fusing the features by SVM.

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Section: Jitter and Shimmermentioning

confidence: 99%

Using Jitter and Shimmer in speaker verification

Farrús

Hernando

2009

IET Signal Process.

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“…The signal acquisition was carried out using PRAAT software by selecting the sampling frequency as 44,100 Hz and channel mode as mono. [12][13][14] The voice signals were saved on the hard disk in WAVE format and acoustic analysis was carried out on selected 2 sec steady portion of the acquired voice signal.…”

Section: Databasementioning

confidence: 99%

“…[18] A free shareware program "PRAAT" was used to quantify these time domain features. [14] Figure 1 show the different steps involved in the calculation of LPC-MFCCs. The human speech production mechanism has the tendency to attenuate the components present in high frequency range, therefore initially the voice signal is pre-emphasized using a 1st order high pass finite impulse response (FIR) filter using Eq.(1).…”

Section: Time Domain Featuresmentioning

confidence: 99%

A hybrid model for neurological disordered voice classification using time and frequency domain features

K¹,

Holi

2015

AIR

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Different neurological disorders may lead to speech related problems, due to paralysis in vocal fold or weakness of the related muscles. This may modify the acoustic characteristics of the subject's voice which may provide important information for detecting certain neurological diseases. The vowel phonation which is acoustically informative and uttered by the patient with not much difficulty is collected and various acoustic features are extracted by time domain and frequency domain techniques. The use of all these features for classification may lead to a large feature space, which may lead to complexity. Hence to avoid this, in the present work experimentation is done by fusing different classifiers which are fed with features extracted from different domains. The time domain and frequency domain features are given to Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) respectively, and the intermediate decision of these classifiers is given to another SVM to identify the voice signal as normal or diseased. It is observed that this hybrid classifier model has shown some improvement with a classification accuracy of 91.43% compared to single GMM classifier with an accuracy of classification of 90% with frequency domain features as input.

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“…Human speech is represented by various features, and measurement of relevant speech features of production in speech impairment were reported in existing literatures [13] [14]. Wertzner et al [14] state that the most important vocal speech features for clinical use are the measurement of vocal extension profile such as frequencies and intensity, noise, acoustic spectrograph; fundamental and formant frequencies and perturbation index; jitter and shimmer.…”

Section: Introductionmentioning

confidence: 99%

“…Wertzner et al [14] state that the most important vocal speech features for clinical use are the measurement of vocal extension profile such as frequencies and intensity, noise, acoustic spectrograph; fundamental and formant frequencies and perturbation index; jitter and shimmer. In this paper, we have considered formant frequencies (F1, F2), fundamental frequency (F0), intensity, jitter and shimmer to evaluate the effect of changes in these selected features on the intelligibility deficits and ASR performance.…”

Section: Introductionmentioning

confidence: 99%

The Effect Of Changes In Speech Features On The Recognition Accuracy Of ASR System: A Study On The Malay Speech Impaired Children

Rosdi

Mustafa

Salim

et al. 2017

MJCS

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Speech impairments refers to disability that causes the human speech production to deviate from the norm. Although there have been several researches undertaken to identify the differences between non-impaired and impaired speech, little is known about their effects on the speech intelligibility and the performance of ASR systems in recognizing impaired speech of children. This study investigates the speech features of impaired speech in relation to intelligibility deficits and degradation in ASR performance; which includes, formant frequencies, intensity, fundamental frequency (F0) and perturbation features such as jitter and shimmer. As there is no existing speech database for performing the evaluation, we have developed a speech database of speech impaired children and have analysed the impaired speech features. We have identified significant differences in the selected features. We also have identified the relationship between the ASR system's Word Error Rate (WER) of impaired speeches with the speech features. The results show that there are significant differences in F0, jitter and shimmer across the Control Group (CG) and the Speech Impaired Group (SIG). This paper explains the differences between impaired speeches and non-impaired speeches that can be used in developing automated speech recognition system. We have observed that F0 affects the ASR performance and was found to be a significant predictor that influences the accuracy of vowel phonemes /e/ and /u/.

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Analysis of fundamental frequency, jitter, shimmer and vocal intensity in children with phonological disorders

Cited by 51 publications

References 13 publications

Using Jitter and Shimmer in speaker verification

Using Jitter and Shimmer in speaker verification

A hybrid model for neurological disordered voice classification using time and frequency domain features

The Effect Of Changes In Speech Features On The Recognition Accuracy Of ASR System: A Study On The Malay Speech Impaired Children

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