Objective characterization of oesophageal voice supporting medical diagnosis, rehabilitation and monitoring

Garcı́a, Begoña; Ruiz, I.; Méndez, Amaia; Mendezona, M.

doi:10.1016/j.compbiomed.2008.11.009

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Voice Analysis is divided into the recording of the voice of the patient for the purpose of processing, and the calculation of the parameters of voice quality: pitch, jitter, shimmer and signal to noise ratio (García et al [2009]). Furthermore, a perceptual analysis is performed.…”

Section: Methodsmentioning

confidence: 99%

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Computer aided tool for diagnosis of ENT pathologies using digital signal processing of speech and stroboscopic images

2012

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The development of computer software and other technologies greatly facilitates the evaluation of pathological voice patients. This fact allows to reduce exploration time, improves the reproducibility of results and creates the possibility of test protocol standardization needed for the intercommunication between the different voice specialists. The proposed application encompasses the most important aspects which should be taken into account regarding dysphonic patients. It is a multidimensional scope which involves subjective questionnaires and perceptual, aerodynamic, acoustic and stroboscopic evaluations. In this system, the authors have designed and created simple tools for recording and automatic acoustic analysis for the acquisition and edition of stroboscopic images. The purpose is to work with all necessary tools running on a single application, without having to export and import data from other computer programs. Therefore, the objective is to synthetize the basic voice and the exploration of the vocal folds, simplifying it through the design of a program which helps us to analyze step-by-step each aspect of the vocal pathology. The evaluation of the tool has been performed by the otolaryngologists through periodical (medical) appointments on 25 patients for one year a year, and the results are promising either for the professionals as well as for the patients which receive a detailed report with the objective information concerning the features of their voice and vocal cords.

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Section: Methodsmentioning

confidence: 99%

“…To include different digital signal processing algorithms developed by the authors in previous works in the tool (Méndez et al Mendez et al [2009]; Mendez et al [2012]), (García et al [2009]).…”

Section: Introductionmentioning

confidence: 99%

Computer aided tool for diagnosis of ENT pathologies using digital signal processing of speech and stroboscopic images

2012

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“…With this cepstral approximation to the pitch, they divide the whole pitch range in frequency bands of 20/30Hz on which different parameters are set in order to be used in the core functions to identity correctly the pitch marks. Those parameters are: where 'p' is the value of the cepstral estimation of the pitch, is is the sample frequency of the signal and 'win' the window length [12].…”

Section: Methodsmentioning

confidence: 99%

Corrective algorithm for esophageal voice cycle detection

Isasi

Zapirain

Zorrilla

2010

The 10th IEEE International Symposium on Signal Processing and Information Technology

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In this paper a corrective solution for more accurate esophageal voice cycle marks detection is presented While the algorithm developed in a previous work adapts the MDVP voice period marks to esophageal voice, it shows lacks when analyzing unsteady /a/ vowel samples and with high levels of noise. The proposed solution is aimed to iteratively correct the parameters (nmin and threshold) used in the previous algorithm. The corrective system filters the noise of voice recordings and then uses the output of the previous algorithm to calculate the most reliable fundamental frequency, based on the most stable part of the given signal. The results were acquired by comparing the pitch values of the previous algorithm with and without the proposed solution and the calculating the error against the real value. The corrective solution presents a considerable improvement in the accuracy of the voice cycle detection algorithm by reducing the standard deviation of the error dataset from 4.591 to 0.593 and the mean error value from 2.459 down to 0.370. Furthermore, it reduces up to a 54% all errors above J%.

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“…The E speech has low quality due to irregular vibration of Paryngo-esophageal (PE) segments, and enhancement of E speech has been extensively treated by LPC analysis/synthesis [18], [19], [20], [21], [22], statistical methods [23], [24], [25] and detailed analysis of E speech by our group can be consulted from [26], [27], [28], [29], [30], [31], [32]. The Kalman filter has been used for enhancement of E speech along with pole stabilization and, improvement observed over LPC analysis/synthesis framework [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Subband modulator Kalman filtering for single channel speech enhancement

Ishaq

Zapirain

Shahid

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Abstract-This paper presents a single channel speech enhancement technique based on sub-band modulator Kalman filtering for laryngeal (normal) and alaryngeal (Esophageal speech) speech signals. The noisy speech signal is decomposed into sub-bands and subsequently each sub-band is demodulated into its modulator and carrier components. Kalman filter is applied to modulators of all sub-bands without altering the carriers. Performance of the proposed system has been validated by Mean Opinion Score (MOS) for laryngeal and Harmonic to Noise Ratio (HNR) for alaryngeal speech. An improvement of 20% has been observed in MOS over sub-band Kalman filtering for laryngeal speech, while 3 to 4 dB enhancement in HNR has been observed for alaryngeal speech over the full-band Kalman filtering.

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Objective characterization of oesophageal voice supporting medical diagnosis, rehabilitation and monitoring

Cited by 6 publications

References 10 publications

Computer aided tool for diagnosis of ENT pathologies using digital signal processing of speech and stroboscopic images

Computer aided tool for diagnosis of ENT pathologies using digital signal processing of speech and stroboscopic images

Corrective algorithm for esophageal voice cycle detection

Subband modulator Kalman filtering for single channel speech enhancement

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