Evaluation of objective measures for speech enhancement

Objective measures of performance are seldom perfect. In response, incentive contracts often include important subjective components that mitigate incentive distortions caused by imperfect objective measures. This paper explores the combined use of subjective and objective performance measures in (respectively) implicit and explicit incentive contracts. Naturally, objective and subjective measures often are substitutes, sometimes strikingly so: we show that if objective measures are sufficiently close to perfect then no implicit contracts are feasible (because the firm's fallback position after reneging on an implicit contact is too attractive). We also show, however, that objective and subjective measures can reinforce each other: if objective measures become more accurate then in some circumstances the optimal contract puts more weight on subjective measures (because the improved objective measures increase the value of the ongoing relationship, and so reduce the firm's incentive to renege). We also analyze the use of subjective weights on objective performance measures, and provide case-study evidence consistent with our analyses.

Subjective versus objective measures of distress, arousal and symptom burden in patients with functional seizures and other functional neurological symptom disorder presentations: A systematic review

Evaluation of an auditory masked threshold noise suppression algorithm in normal-hearing and hearing-impaired listeners

Outcome Reporting in Spine Surgery: A Review of Historical and Emerging Trends

Objective vs Subjective Measurements of Palatine Tonsil Size in Patients with OSA

In this paper, we present a new Speech Enhancement (SE) technique capable of running on a smartphone, as an assistive device for hearing aids (HAs). The developed method incorporates the coherence between the speech and noise signals to obtain a SE gain function which is used in conjunction with the gain function obtained by Spectral Subtraction using adaptive gain averaging. SE using coherence based gain function is found to suppress the background noise well, while inducing speech distortion. On the other hand, SE using Spectral Subtraction improves speech quality with tolerable speech distortion, but introduces background musical noise for certain noise types. The weighted fusion of the two gain functions strikes a balance between noise suppression and speech distortion. Also it allows the user to control the weighting factor based on the noisy environment and their comfort level of hearing. The developed method is computationally fast and operates in real-time. The proposed method was evaluated for machinery, babble, and car noise types, using both objective and subjective measures for both quality and intelligibility of the enhanced speech. The results show significant improvements in comparison with stand-alone Spectral Subtraction with weighted gain averaging SE methods.

BackgroundThe physical and mental demands of handball during training or competition often lead to fatigue which can impair performance. Many attempts have been made to assess the level of fatigue in athletes either by objective (neuromuscular performance) or subjective (questionnaires) measures, however, their interplay over short-, mid-, and long-term periods is currently unknown. Knowledge about both types of assessments is important as load management by coaches is traditionally based on direct adjustments following a training session, adjustments of content structure of training weeks between games, as well as adjustments of load management over the entire competitive season. Thus, this study aimed to investigate the interplay between objective and subjective fatigue measures at multiple test times throughout a handball season.MethodsA total of 100 highly trained (Tier level 3) adolescent or young adult team handball players (23 females) took part in the study. The parameters tested were the Leg Recovery Test (LRT score) which is based on the countermovement jump height (CMJ) and was assessed by a commercial wristwatch (Polar Vantage V2) as an objective measure of neuromuscular fatigue. Additionally, on a subjective level, questionnaire-based athlete self-report measures, specifically the Perceived Recovery Status Scale (PRSS) and the Short Scale of Recovery and Strain (KEB) were assessed. We used non-parametric tests to detect differences between relevant test time points (short-term: immediately following one handball-specific training session, i.e., from T0 to T1; mid-term: over the course of three consecutive training days, i.e., from T0 to T2; long-term: over the course of 8 months of training, i.e., from T0 to T12) and linear mixed models to evaluate the interplay between objective (LRT score) and subjective (KEB score and PRSS score) measures of fatigue across one season.ResultsNon-parametric tests showed that CMJ height (p = .012) and the KEB (p < .001) were higher at T1 compared to T0 for the short-term assessment. Over the course of three consecutive training days (i.e., mid-term assessment), the CMJ height score decreased (T0 to T2: p < .001; T1 to T2: p = .018) and the PRSS score (T0 to T2: p < .001; T1 to T2: p = .003) increased. Linear mixed models revealed no significant effects of KEB or PRSS score on LRT score (i.e., CMJ height) for the short- and mid-term assessments. In terms of the long-term assessments, we detected no general direct or interaction effects of PRSS score, workload, and test time point on LRT score, except for an interaction between PRSS score and workload on LRT score (p = .032), which indicates a workload-dependent association between PRSS and the objective fatigue measure (LRT score).ConclusionAthlete self-reported measures of fatigue indicated significantly higher cumulative fatigue after both short- and mid-term periods, whereas this increase was observed in the LRT score only during the mid-term period. Furthermore, the absence of a relationship between the objective and subjective measures of fatigue during short- and mid-term periods suggests that these measures assess distinct types of fatigue. In the long-term assessments, the significant interaction between the PRSS score and workload on the LRT score suggests that higher workloads are associated with an increased correlation between subjective (PRSS score) and objective (LRT score) measures of fatigue. This indicates that perceived fatigue may be a more sensitive indicator of fatigue, which can be managed to maintain high levels of neuromuscular performance (LRT score). However, with higher workloads (>10 h per week), associations between the objective and subjective measures become apparent, suggesting that workload serves as a common factor influencing overall fatigue.

This study investigated the clinical utility of the combined use of objective and subjective measures of attention-deficit/hyperactivity disorder (ADHD) prepharmacological and postpharmacological treatment. Adults with ADHD (N = 77) completed the Quantified Behavioral Test, self-ratings of ADHD-related symptoms, and quality of life measures pretreatment and posttreatment. The use of objective and subjective measures of ADHD-related symptoms during initiation and follow-up of pharmacological treatment resulted in significant improvements in quality of life after 6 months. Both objective and subjective measures captured changes in ADHD-related symptoms, with more patients showing clinically relevant treatment effects on objective measures. Convergence rates between objective and subjective measures were low to moderate, and improvements on these measures correlated with increased quality of life. Objective and subjective measures of ADHD capture important components of the condition. The findings from this study have important implications for clinical practice.

Subjective and objective measures available at the time of medical school admission were related to subjective and objective clinical performance measures during medical school. Strategies were developed for coding narrative faculty comments from admissions interviews and clinical performance evaluations. Factor analysis was used to examine underlying structures for both admission and clinical performance measures. Summed scores were calculated to represent each factor, and multiple regression was used in predicting each of the clinical factors from the admission factors. Multiple regression showed that admission interview comments best predict narrative clerkship performance, while objective scores best predict an objective measure of clinical knowledge. Conclusions were: (a) narrative information can be coded reliably. (b) Objective and subjective measures are distinct, identifiable structures both at admission and during the third year of medical school. (c) Prediction formulas will vary depending on what outcome variables are chosen.

Speech Enhancement (SE) system deals with improving the perceptual quality and preserving the speech intelligibility of the noisy mixture. The Time-Frequency (T-F) masking-based SE using the supervised learning algorithm, such as a Deep Neural Network (DNN), has outperformed the traditional SE techniques. However, the notable difference observed between the oracle mask and the predicted mask, motivates us to explore different deep learning architectures. In this paper, we propose to use a Convolutional Neural Network (CNN)-based Generative Adversarial Network (GAN) for inherent mask estimation. GAN takes an advantage of the adversarial optimization, an alternative to the other Maximum Likelihood (ML) optimization-based architectures. We also show the need for supervised T-F mask estimation for effective noise suppression. Experimental results demonstrate that the proposed T-F mask-based SE significantly outperforms the recently proposed end-to-end SEGAN and a GAN-based Pix2Pix architecture. The performance evaluation in terms of both the predicted mask and the objective measures, dictates the improvement in the speech quality, while simultaneously reducing the speech distortion observed in the noisy mixture.

Automatic separation of speech from noise and segregation of overlapping co-channel speech are two of the most challenging problems in speech processing. In previous work, we have developed algorithms to both enhance noisy speech and segregate overlapping speech streams. Our single-channel speech enhancement and speech segregation algorithms have shown better performance than other reported algorithms for automatic speech recognition. Additionally, objective evaluation scores of perceptual quality have shown a significant improvement following processing by our algorithms. In this study, we focus on subjective evaluation of these algorithms for human listeners. We investigate the intelligibility and quality of speech from our algorithm on normal-hearing listeners, cochlear implant users, and hearing impaired subjects. Our preliminary results indicate a significant improvement in the perceptual quality of the speech signal after being processed by our algorithm and suggest that the proposed algorithms can be used as a pre-processing block within the signal processing in hearing aid devices.

Speech communicated is adversely affected by environmental noise. It is important to process the speech and reduce noise for better understanding. Speech enhancement or noise reduction is useful to provide comfort for human or machine listening. Traditional algorithms provide better noise reduction and better-quality speech. Due to the non-stationary nature of noise and the quasi-stationary nature of speech, the traditional methods are proven inadequate in achieving high-quality speech. Later statistical estimators based on Gaussian, and super-Gaussian Probability Density Function (PDF) assumption further improved the enhancement performance. But still, non-stationary noise nature introduces artifacts in processed signal and results in decreased performance. It is observed that neural network approaches and the factorization approach provide better performance even under non-stationary noises by proper training and large database. Different features result in variations in output performance under unseen noise and speaker conditions. It is important to understand the importance and advantages of traditional methods, statistical estimators, and neural network approaches performances. To select the suitable method for a required application, it is essential to consider the trade-off between quality and distortion. In this work, the importance of speech enhancement methods is discussed. Performance measures used for understanding the speech enhancement like Signal to Noise Ratio (SNR), Segmental SNR, Log-Likelihood Ratio (LLR), Weighted Spectral Slope (WSS), Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), Signal to Distortion Ratio (SDR) and Mean Opinion Score (MOS), are given. Highlights of important results are discussed for analyzing better speech enhancement methods for the required application. In this work, performance is compared using objective and subjective performance measures. Simulation results show superior performance when neural network is employed in statistical estimators.

This paper presents the results of a statistical correlation study between a data base of subjective speech quality measures and a data base of objective speech quality measures. Both data bases are derived from approximately 18 hours of coded and distorted speech. The subjective test used was the Diagnostic Acceptability Measure (DAM), a parametric speech quality test developed at the Dynastat Corporation. The objective measures included approximately 1500 parametric variations of many commonly suggested objective measures. This paper summarizes the performance of the objective measures in predicting the subjective results.

BackgroundPrior studies have reported associations between neighborhood characteristics and late‐life cognition. These studies assessed neighborhood using either objective (e.g., area‐level data from public database) or subjective (individual‐level perception) measures. However, little research has been done to examine effects of objective and subjective measures simultaneously. The present study aims to investigate the independent associations of objective and subjective neighborhood characteristics with cognitive change in older adults.MethodParticipants include 316 individuals in the Einstein Aging Study (mean age = 77; 67% females; 45% non‐Hispanic Whites, 40% non‐Hispanic Blacks). Subjective neighborhood measures included perceived neighborhood safety, aesthetic quality, social cohesion, and availability of healthy foods (range = 1 poor to 5 excellent). Objective neighborhood measures included violent crime, neighborhood deprivation, volunteering rates, percentage of healthy food retailers, and low access to healthy food. Processing speed was assessed using the brief, smartphone‐administered Symbol Search Test, and was completed 6x/day over two weeks; this was repeated annually from 2017 to 2022. To account for non‐linear retest/practice effects, double negative exponential models were used to estimate the relation of neighborhood measures to asymptotic performance (i.e., fastest response time) at baseline and to change in asymptotic performance over time.ResultMost subjective and objective neighborhood measures were significantly correlated with each other (Table 1). In models containing both objective and subjective neighborhood measures, better perceived safety was associated with greater annual improvement in asymptotic performance (40 milliseconds/year for one point increase in safety) while the objective measure of violent crime was not. Perceived availability of healthy foods was independently associated with a faster baseline asymptotic processing speed (estimate = 160 milliseconds) and with greater annual improvement rate (estimate = 54 milliseconds/year) while the objective measures of food environment were not (Table 2).ConclusionSubjective rather than objective neighborhood characteristics were strongly associated with annual rates of change in processing speed over five years in community‐dwelling older adults. Perception of one’s neighborhood may be a more proximal predictor of cognitive health as it may reflect an individual’s lived experiences and determine one’s psychological states and health‐related behaviors. Alternatively, more fine‐grained objective neighborhood measures (e.g., individual‐level) would be necessary to better characterize objective neighborhood environments.

On using multivariate polynomial regression model with spectral difference for statistical model-based speech enhancement