Smartphone Recordings are Comparable to “Gold Standard” Recordings for Acoustic Measurements of Voice

Awan, Shaheen N.; Shaikh, Mohsin Ahmed; Awan, Jordan; Abdalla, Ibrahim; Lim, Kelvin O.; Misono, Stephanie

doi:10.1016/j.jvoice.2023.01.031

Cited by 18 publications

(9 citation statements)

References 31 publications

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“…A substantial device effect was detected in a comparable study [68] for low versus high spectral ratio (L/H Ratio) (dB) in both vowel and phrase contexts, as well as for the cepstral spectral index of dysphonia (CSID) in the sentence context. It was discovered that independent of context, the device had a little influence on CPP (dB).…”

Section: Discussionmentioning

confidence: 79%

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

et al. 2023

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Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency (f0), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.

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Section: Discussionmentioning

confidence: 79%

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

et al. 2023

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“…We extracted intensity-related, frequency-related, and periodicity metrics from simultaneous recordings and calculated differences, agreement intervals, and correlation coefficients between microphones. Based on previous findings, we hypothesize that agreement intervals between microphone set ups would be smaller than the expected clinical effect (thus acceptable) for f0 and for intensity-related timing measurements but not for metrics of perturbation-periodicity or other frequency-related measurements (16–18, 25, 26). Finally, we hypothesize that despite possible differences in measurements, the consistent use any of the tested microphones would yield a similar differentiation between a group of dysarthric (mild and sub-clinical) and non-dysarthric speakers.…”

Section: Introductionmentioning

confidence: 83%

“…Because of the largely systematic nature of the differences, a post-recording mathematical correction accounting for the microphone’s frequency response improved the accuracy for discriminating between healthy and pathological voices. In a recent study, Awan et al compared playback speech recorded by four modern smartphones, which had similar frequency responses, and a reference microphone and observed a correlational equivalence in measured CPP but a large device-effect on measured spectral tilt (25). In a similar study testing consumer and professional-grade head-worn microphones, this time with different frequency response curves, Awan et al highlighted that differences in measured CPP were not correlated to frequency response or to microphone’s sensitivity around 100-200Hz (26) in contrast to earlier assumptions (22).…”

Section: Introductionmentioning

confidence: 99%

Plug-and-play microphones for recording speech and voice with smart devices

Noffs,

Cobler-Lichter,

Perera

et al. 2023

Preprint

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INTRODUCTIONSmart devices are widely available and capable of quickly recording and uploading speech segments for health-related analysis. The switch from laboratory recordings with professional-grade microphone set ups to remote, smart device-based recordings offers immense potential for the scalability of voice assessment. Yet, a growing body of literature points to a wide heterogeneity among acoustic metrics for their robustness to variation in recording devices. The addition of consumer-grade plug-and-play microphones has been proposed as a possible solution. The aim of our study was to assess if the addition of consumer-grade plug-and-play microphones increases the acoustic measurement agreement between ultra-portable devices and a reference microphone.METHODSSpeech was simultaneously recorded by a reference high-quality microphone commonly used in research, and by two configurations with plug-and-play microphones. Twelve speech-acoustic features were calculated using recordings from each microphone to determine the agreement intervals in measurements between microphones. Agreement intervals were then compared to expected deviations in speech in various neurological conditions. Additionally, each microphone’s response to speech and to silence were characterized through acoustic analysis to explore possible reasons for differences in acoustic measurements between microphones. Lastly, the statistical differentiation of two groups, neurotypical and people with Multiple Sclerosis, using metrics from each tested microphone was compared to that of the reference microphone.RESULTSThe two consumer-grade plug-and-play microphones favoured high frequencies (mean centre of gravity difference ≥ +175.3Hz) and recorded more noise (mean difference in signal-to-noise ≤ -4.2dB) when compared to the reference microphone. Between consumer-grade microphones, differences in relative noise were closely related to distance between the microphone and the speaker’s mouth. Agreement intervals between the reference and consumer-grade microphones remained under disease-expected deviations only for fundamental frequency (f0, agreement interval ≤0.06Hz),f0instability (f0CoV, agreement interval ≤0.05%) and for tracking of second formant movement (agreement interval ≤1.4Hz/millisecond). Agreement between microphones was poor for other metrics, particularly for fine timing metrics (mean pause length and pause length variability for various tasks). The statistical difference between the two groups of speakers was smaller with the plug-and-play than with the reference microphone.CONCLUSIONMeasurement off0and F2 slope were robust to variation in recording equipment while other acoustic metrics were not. Thus, the tested plug-and-play microphones should not be used interchangeably with professional-grade microphones for speech analysis. Plug-and-play microphones may assist in equipment standardization within speech studies, including remote or self-recording, possibly with small loss in accuracy and statistical power as observed in the current study.

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“…However, it should be noted that such an application would once again require a proper training phase, which means a comprehensive dataset of these different kinds of diseases should be used. Furthermore, regarding possible questions about the quality of recordings performed through mobile devices, it should be highlighted that smartphone microphones have been deemed to perform exceptionally well when the goals are healthcare applications [ 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques

Di Cesare,

Perpetuini,

Cardone

et al. 2024

Sensors

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life.

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Smartphone Recordings are Comparable to “Gold Standard” Recordings for Acoustic Measurements of Voice

Cited by 18 publications

References 31 publications

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones

Plug-and-play microphones for recording speech and voice with smart devices

Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques

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