Spectral Moments of the Long-term Average Spectrum: Sensitive Indices of Voice Change After Therapy?

Tanner, Kristine; Roy, Nelson; Ash, Andrea C.; Buder, Eugene H.

doi:10.1016/j.jvoice.2004.02.005

Cited by 52 publications

(42 citation statements)

References 41 publications

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“…However, the relationship between these acoustic measures and perceptual judgments is still not clear. 31 Systematic evaluation of the anatomic location of the electrode contact and its effects on speech showed that electrodes placed medial to the left STN were worse for speech intelligibility than electrodes inside the STN, confirming results from other studies. [32][33][34] Equally, information on the particular STN segment in which the active contact is located and speech outcome is scarce.…”

Section: Factors Associated With Speech Deterioration Following 1 Yeasupporting

confidence: 84%

Effects of subthalamic stimulation on speech of consecutive patients with Parkinson disease

Tripoliti¹,

Zrinzo²,

et al. 2011

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Objective: Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for advanced Parkinson disease (PD). Following STN-DBS, speech intelligibility can deteriorate, limiting its beneficial effect. Here we prospectively examined the short-and long-term speech response to STN-DBS in a consecutive series of patients to identify clinical and surgical factors associated with speech change.Methods: Thirty-two consecutive patients were assessed before surgery, then 1 month, 6 months, and 1 year after STN-DBS in 4 conditions on-and off-medication with on-and offstimulation using established and validated speech and movement scales. Fifteen of these patients were followed up for 3 years. A control group of 12 patients with PD were followed up for 1 year. Results:Within the surgical group, speech intelligibility significantly deteriorated by an average of 14.2% Ϯ 20.15% off-medication and 16.9% Ϯ 21.8% on-medication 1 year after STN-DBS. The medical group deteriorated by 3.6% Ϯ 5.5% and 4.5% Ϯ 8.8%, respectively. Seven patients showed speech amelioration after surgery. Loudness increased significantly in all tasks with stimulation. A less severe preoperative on-medication motor score was associated with a more favorable speech response to STN-DBS after 1 year. Medially located electrodes on the left STN were associated with a significantly higher risk of speech deterioration than electrodes within the nucleus. There was a strong relationship between high voltage in the left electrode and poor speech outcome at 1 year. Conclusion:The effect of STN-DBS on speech is variable and multifactorial, with most patients exhibiting decline of speech intelligibility. Both medical and surgical issues contribute to deterioration of speech in STN-DBS patients. Classification of evidence:This study provides Class III evidence that STN-DBS for PD results in deterioration in speech intelligibility in all combinations of medication and stimulation states at 1 month, 6 months, and 1 year compared to baseline and to control subjects treated with best medical therapy. Neurology

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Section: Factors Associated With Speech Deterioration Following 1 Yeasupporting

confidence: 84%

Effects of subthalamic stimulation on speech of consecutive patients with Parkinson disease

Tripoliti¹,

Zrinzo²,

et al. 2011

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“…For instance, long term average spectrum (LTAS) has been used in the clinical domain for voice quality measurement. It is employed for example for the early detection of voice pathology [26] or Parkinson disease [27], or for evaluating the effect of speech therapy or surgery on the voice quality [28]. In addition to assessing voice quality, LTAS has also been used to investigate voice characteristics.…”

Section: Motivationmentioning

confidence: 99%

Long-Term Spectral Statistics for Voice Presentation Attack Detection

Muckenhirn

Korshunov

Magimai-Doss

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Automatic speaker verification systems can be spoofed through recorded, synthetic or voice converted speech of target speakers. To make these systems practically viable, the detection of such attacks, referred to as presentation attacks, is of paramount interest. In that direction, this paper investigates two aspects: (a) a novel approach to detect presentation attacks where, unlike conventional approaches, no speech signal related assumptions are made, rather the attacks are detected by computing first order and second order spectral statistics and feeding them to a classifier, and (b) generalization of the presentation attack detection systems across databases. Our investigations on Interspeech 2015 ASVspoof challenge dataset and AVspoof dataset show that, when compared to the approaches based on conventional short-term spectral processing, the proposed approach with a linear discriminative classifier yields a better system, irrespective of whether the spoofed signal is replayed to the microphone or is directly injected into the system software process. Cross-database investigations show that neither the short-term spectral processing based approaches nor the proposed approach yield systems which are able to generalize across databases or methods of attack. Thus, revealing the difficulty of the problem and the need for further resources and research.

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“…The Long Term Average Spectrum (LTAS) is used in the clinical domain as a voice quality measurement. It is employed for example for the early detection of voice pathology [22] or Parkinson disease [23] or for evaluating the effect of speech therapy or surgery on the voice quality [24]. In addition to assessing voice quality, LTAS has also been used to investigate voice characteristics.…”

Section: A Motivationmentioning

confidence: 99%

Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification

Muckenhirn

Magimai-Doss

Marcel

2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Abstract-In recent years, there has been a growing interest in developing countermeasures against non zero-effort attacks for speaker verification systems. Until now, the focus has been on logical access attacks, where the spoofed samples are injected into the system through a software-based process. This paper investigates a more realistic type of attack, referred to as physical access or presentation attacks, where the spoofed samples are presented as input to the microphone. To detect such attacks, we propose a binary classifier based approach that uses longterm spectral statistics as feature input. Experimental studies on the AVspoof database, which contains presentation attacks based on replay, speech synthesis and voice conversion, shows that the proposed approach can yield significantly low detection error rate with a linear classifier (half total error rate of 0.038%). Furthermore, an investigation on Interspeech 2015 ASVspoof challenge dataset shows that it is equally capable of detecting logical access attacks.

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Spectral Moments of the Long-term Average Spectrum: Sensitive Indices of Voice Change After Therapy?

Cited by 52 publications

References 41 publications

Effects of subthalamic stimulation on speech of consecutive patients with Parkinson disease

Effects of subthalamic stimulation on speech of consecutive patients with Parkinson disease

Long-Term Spectral Statistics for Voice Presentation Attack Detection

Presentation Attack Detection Using Long-Term Spectral Statistics for Trustworthy Speaker Verification

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