Esophageal Voices: Glottal Flow Restoration

Garcı́a, Begoña; Vicente, J.; Ruiz, I.; Alonso, A.; Loyo, E.

doi:10.1109/icassp.2005.1415965

Cited by 16 publications

(3 citation statements)

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“…Furthermore, testing different types of CNN and training models might further improve the voice pathology detection approach. Future work could include the application of this method to esophageal voices [46][47][48] as patients that had a larynx cancer often have a low intelligibility voice and this reduces a lot their social communication skills. Any contribution to this topic of the esophageal speech will be very helpful for patients with a laryngectomy.…”

Section: Discussionmentioning

confidence: 99%

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

et al. 2020

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Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.

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Section: Discussionmentioning

confidence: 99%

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

et al. 2020

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“…In fact, it can be very useful in cases of early detection of the cancer where the laryngeal voice of the patients can have a very poor intelligibility and an increased level of noise [3]. This being the main aim, a number of secondary objectives could also be achieved:…”

Section: Objectivesmentioning

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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Esophageal Voices: Glottal Flow Restoration

Cited by 16 publications

References 1 publication

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

Voice Pathology Detection and Classification Using Convolutional Neural Network Model

Corrective algorithm for esophageal voice cycle detection

Subband modulator Kalman filtering for single channel speech enhancement

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