External anal sphincter electromyographic patterns in multiple system atrophy: implications for diagnosis, clinical correlations, and novel insights into prognosis

Multiple system atrophy (MSA) is a sporadic, progressive, adult-onset, neurodegenerative disorder characterized by autonomic dysfunction symptoms, parkinsonian features and cerebellar signs in various combinations. An early MSA diagnosis is of the utmost importance for a proper prevention and management of its potentially fatal complications leading to the poor prognosis of these patients. The current diagnostic criteria incorporate several clinical red flags and magnetic resonance imaging markers supporting MSA diagnosis. Nonetheless, especially in the early disease stage, it can be challenging to differentiate MSA from mimic disorders, in particular Parkinson’s disease (PD). Electromyography (EMG) of the external anal sphincter (EAS) represents a useful neurophysiological tool for the differential diagnosis, since it can provide indirect evidence of Onuf’s nucleus degeneration, which is a pathological hallmark of MSA. However, the diagnostic value of EAS EMG has been a matter of debate for three decades due to controversial reports in the literature. After a brief overview on the electrophysiological methodology, we critically analyzed the available knowledge on the diagnostic role of EAS EMG and discussed the conflicting evidence on the clinical correlations of neurogenic abnormalities found at EAS EMG. This study aimed to explore the diagnostic and prognostic value of a novel classification of EAS EMG patterns, and their correlations with clinical features and cardiovascular autonomic function in MSA. We retrospectively collected clinical data and EAS EMG findings in 72 patients with MSA and 21 with PD. Sixty-one and 56 MSA patients also underwent cardiovascular reflex tests and 24-hour blood pressure monitoring, respectively. We ascertained the survival times of 49 MSA patients who died during follow-up. Through evaluation of spontaneous activity, motor unit action potential (MUAP) duration and recruitment, we identified four EAS EMG patterns: normal findings (pattern I); mild neurogenic damage (pattern II); moderate neurogenic damage (pattern III); severe neurogenic damage (pattern IV). Pattern I was frequently observed in PD patients, while it was associated with prolonged survival when identified in a few MSA patients. Conversely, patterns II, III and IV were predominant in MSA. Subjects with MSA and EAS EMG abnormalities often showed fecal incontinence and urogenital symptoms, which were frequently present at disease onset when MUAP recruitment was impaired. Abnormal EAS EMG patterns correlated with MSA diagnosis (p < 0.001), with a sensitivity of 88.9%, specificity of 85.7%, and odds ratio of 48.0 (95% confidence interval: 11.5–199.8). Pattern IV was associated with the highest likelihood of MSA diagnosis (p < 0.001), and with the worst prognosis in the MSA cohort (vs. pattern I, p < 0.001; vs. pattern II, p = 0.001; vs. pattern III, p = 0.007). EAS EMG patterns were not related to motor impairment or cardiovascular autonomic function in MSA. In conclusion, the increasing severity of EAS EMG patterns paralleled diagnostic accuracy and survival in MSA. EAS EMG patterns correlated with symptom type at disease onset and with prevalence of urogenital symptoms and fecal incontinence. Prognostic findings of our novel classification of EAS EMG patterns could pave the way towards the implementation of this neurophysiological technique for survival prediction in MSA patients

Can spoofing countermeasure and speaker verification systems be jointly optimised?

Spoofing countermeasure (CM) and automatic speaker verification (ASV) sub-systems can be used in tandem with a backend classifier as a solution to the spoofing aware speaker verification (SASV) task. The two sub-systems are typically trained independently to solve different tasks. While our previous work demonstrated the potential of joint optimisation, it also showed a tendency to over-fit to speakers and a lack of sub-system complementarity. Using only a modest quantity of auxiliary data collected from new speakers, we show that joint optimisation degrades the performance of separate CM and ASV sub-systems, but that it nonetheless improves complementarity, thereby delivering superior SASV performance. Using standard SASV evaluation data and protocols, joint optimisation reduces the equal error rate by 27\% relative to performance obtained using fixed, independently-optimised sub-systems under like-for-like training conditions.Comment: Accepted to ICASSP 2023. Code will be available soo

On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification

The spoofing-aware speaker verification (SASV) challenge was designed to promote the study of jointly-optimised solutions to accomplish the traditionally separately-optimised tasks of spoofing detection and speaker verification. Jointly-optimised systems have the potential to operate in synergy as a better performing solution to the single task of reliable speaker verification. However, none of the 23 submissions to SASV 2022 are jointly optimised. We have hence sought to determine why separately-optimised sub-systems perform best or why joint optimisation was not successful. Experiments reported in this paper show that joint optimisation is successful in improving robustness to spoofing but that it degrades speaker verification performance. The findings suggest that spoofing detection and speaker verification sub-systems should be optimised jointly in a manner which reflects the differences in how information provided by each sub-system is complementary to that provided by the other. Progress will also likely depend upon the collection of data from a larger number of speakers.Comment: Accepted to IberSPEECH 2022 Conferenc

Speaker anonymization using neural audio codec language models

The vast majority of approaches to speaker anonymization involve the extraction of fundamental frequency estimates, linguistic features and a speaker embedding which is perturbed to obfuscate the speaker identity before an anonymized speech waveform is resynthesized using a vocoder. Recent work has shown that x-vector transformations are difficult to control consistently: other sources of speaker information contained within fundamental frequency and linguistic features are re-entangled upon vocoding, meaning that anonymized speech signals still contain speaker information. We propose an approach based upon neural audio codecs (NACs), which are known to generate high-quality synthetic speech when combined with language models. NACs use quantized codes, which are known to effectively bottleneck speaker-related information: we demonstrate the potential of speaker anonymization systems based on NAC language modeling by applying the evaluation framework of the Voice Privacy Challenge 2022.Comment: Submitted to ICASSP 202

Review 1: "SARS-CoV-2 invades cognitive centers of the brain and induces Alzheimer's-like neuropathology"

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Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systems

We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs). By introducing convolutional noise using an optimised linear time-invariant filter, Malafide attacks can be used to compromise CM reliability while preserving other speech attributes such as quality and the speaker's voice. In contrast to other adversarial attacks proposed recently, Malafide filters are optimised independently of the input utterance and duration, are tuned instead to the underlying spoofing attack, and require the optimisation of only a small number of filter coefficients. Even so, they degrade CM performance estimates by an order of magnitude, even in black-box settings, and can also be configured to overcome integrated CM and ASV subsystems. Integrated solutions that use self-supervised learning CMs, however, are more robust, under both black-box and white-box settings.Comment: Accepted at INTERSPEECH 202

Scraping Airlines Bots: Insights Obtained Studying Honeypot Data

Airline websites are the victims of unauthorised online travel agencies and aggregators that use armies of bots to scrape prices and flight information. These so-called Advanced Persistent Bots (APBs) are highly sophisticated. On top of the valuable information taken away, these huge quantities of requests consume a very substantial amount of resources on the airlines' websites. In this work, we propose a deceptive approach to counter scraping bots. We present a platform capable of mimicking airlines' sites changing prices at will. We provide results on the case studies we performed with it. We have lured bots for almost 2 months, fed them with indistinguishable inaccurate information. Studying the collected requests, we have found behavioural patterns that could be used as complementary bot detection. Moreover, based on the gathered empirical pieces of evidence, we propose a method to investigate the claim commonly made that proxy services used by web scraping bots have millions of residential IPs at their disposal. Our mathematical models indicate that the amount of IPs is likely 2 to 3 orders of magnitude smaller than the one claimed. This finding suggests that an IP reputation-based blocking strategy could be effective, contrary to what operators of these websites think today