Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis

Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification

Quality Control in Remote Speech Data Collection

There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers, which deserve further investigation. In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold. DetMCD is a computationally efficient algorithm, which provides a highly robust estimate of the mean and covariance of multivariate data even when 50% of the data are outliers. Experimental results using eight different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in a significant decrease in the effort required for manually controlling the quality of the database

A Parametric Approach for Classification of Distortions in Pathological Voices

In biomedical acoustics, distortion in voice signals, commonly present during acquisition and transmission, adversely affects acoustic features extracted from pathological voice. Information on the type of distortion can help in compensating for its effects. This paper proposes a new approach to detecting four major types of commonly encountered distortion in remote analysis of pathological voice, namely background noise, reverberation, clipping and coding. In this approach, by applying factor analysis to Gaussian mixture model mean supervectors, distortions in variable-duration recordings are modeled by fixed-length, low-dimensional channel vectors. Then, linear discriminant analysis (LDA) is used to remove the remaining nuisance effects in the channel vectors. Finally, two different classifiers, namely support vector machines and probabilistic LDA classify the different types of distortion. Experimental results obtained using Parkinson's voices, as an example of pathological voice, show 11.4% relative improvement in performance over systems which directly use acoustic features for distortion classification

A Supervised Approach to Global Signal-to-Noise Ratio Estimation for Whispered and Pathological Voices

The presence of background noise in signals adversely affects the performance of many speech-based algorithms. Accurate estimation of signal-to-noise-ratio (SNR), as a measure of noise level in a signal, can help in compensating for noise effects. Most existing SNR estimation methods have been developed for normal speech and might not provide accurate estimation for special speech types such as whispered or disordered voices, particularly, when they are corrupted by non-stationary noises. In this paper, we first investigate the impact of stationary and non-stationary noise on the behavior of mel-frequency cepstral coefficients (MFCCs) extracted from normal, whispered and pathological voices. We demonstrate that, regardless of the speech type, the mean and the covariance of MFCCs are predictably modified by additive noise and the amount of change is related to the noise level. Then, we propose a new supervised method for SNR estimation which is based on a regression model trained on MFCCs of the noisy signals. Experimental results show that the proposed approach provides accurate estimation and consistent performance for various speech types under different noise conditions

Robust Bayesian Pitch Tracking Based on the Harmonic Model

Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results. 1 1An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543

A Kalman-based Fundamental Frequency Estimation Algorithm

Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually as- sume that the fundamental frequency and amplitudes are station- ary over a short time frame. In this paper, we propose a Kalman filter-based fundamental frequency estimation algorithm using the harmonic model, where the fundamental frequency and amplitudes can be truly nonstationary by modeling their time variations as first- order Markov chains. The Kalman observation equation is derived from the harmonic model and formulated as a compact nonlinear matrix form, which is further used to derive an extended Kalman filter. Detailed and continuous fundamental frequency and ampli- tude estimates for speech, the sustained vowel /a/ and solo musical tones with vibrato are demonstrated

Quality Control of Voice Recordings in Remote Parkinson’S Disease Monitoring Using the Infinite Hidden Markov Model

The performance of voice-based systems for remote monitoring of Parkinson’s disease is highly dependent on the degree of adherence of the recordings to the test protocols, which probe for specific symptoms. Identifying segments of the signal that adhere to the protocol assumptions is typically performed manually by experts. This process is costly, time consuming, and often infeasible for large-scale data sets. In this paper, we propose a method to automatically identify the segments of signals that violate the test protocol with a high accuracy. In our approach, the signal is first split into variable duration segments by fitting an infinite hidden Markov model (iHMM) to the frames of the signals in the mel-frequency cepstral domain. The complexity of the iHMM is capable of growing jointly with the data allowing us to infer a potentially large (asymptotically infinite) number of different phenomena segmented into different hidden states. Then, we identify the segments that adhere to the test protocol by applying a multinomial naive Bayes classifier to the state indicators of segments. The experimental results show that even by using a small amount of training data, we can achieve around 96% accuracy in identifying short-term protocol violations with a 0.2 s resolution

Oral supplementation of healthy adults with 2'-O-fucosyllactose and lacto-N-neotetraose is well tolerated and shifts the intestinal microbiota

The gut microbiota has been established as an important player influencing many aspects of human physiology. Breast milk, the first diet for an infant, contains human milk oligosaccharides (HMO) that shape the infant's gut microbiota by selectively stimulating the growth of specific bacteria, especially bifidobacteria. In addition to their bifidogenic activity, the ability of HMO to modulate immune function and the gut barrier makes them prime candidates to restore a beneficial microbiota in dysbiotic adults and provide health benefits. We conducted a parallel, double-blind, randomised, placebo-controlled, HMO-supplementation study in 100 healthy, adult volunteers, consuming chemically produced 2'-O-fucosyllactose (2'FL) and/or lacto-N-neotetraose (LNnT) at various daily doses and mixes or placebo for 2 weeks. All participants completed the study without premature discontinuation. Supplementation of 2'FL and LNnT at daily doses up to 20 g was shown to be safe and well tolerated, as assessed using the gastrointestinal symptoms rating scale. 16S rRNA sequencing analysis showed that HMO supplementation specifically modified the adult gut microbiota with the primary impact being substantial increases in relative abundance of Actinobacteria and Bifidobacterium in particular and a reduction in relative abundance of Firmicutes and Proteobacteria. This study provides the first set of data on safety, tolerance and impact of HMO on the adult gut microbiota. Collectively, the results from this study show that supplementing the diet with HMO is a valuable strategy to shape the human gut microbiota and specifically promote the growth of beneficial bifidobacteria