Self-Supervised Learning for Audio-Based Emotion Recognition

Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a major barrier to achieve consistently high-performance models is the paucity of available training labels. Self-supervised learning (SSL) is a family of methods which can learn despite a scarcity of supervised labels by predicting properties of the data itself. To understand the utility of self-supervised learning for audio-based emotion recognition, we have applied self-supervised learning pre-training to the classification of emotions from the CMU- MOSEI's acoustic modality. Unlike prior papers that have experimented with raw acoustic data, our technique has been applied to encoded acoustic data. Our model is first pretrained to uncover the randomly-masked timestamps of the acoustic data. The pre-trained model is then fine-tuned using a small sample of annotated data. The performance of the final model is then evaluated via several evaluation metrics against a baseline deep learning model with an identical backbone architecture. We find that self-supervised learning consistently improves the performance of the model across all metrics. This work shows the utility of self-supervised learning for affective computing, demonstrating that self-supervised learning is most useful when the number of training examples is small, and that the effect is most pronounced for emotions which are easier to classify such as happy, sad and anger. This work further demonstrates that self-supervised learning works when applied to embedded feature representations rather than the traditional approach of pre-training on the raw input space.Comment: 8 pages, 9 figures, submitted to IEEE Transactions on Affective Computin

Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data

Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams

Letter from Peter Washington Martin to James B. Finley

Owing to Bishop McKendree\u27s bad health, Martin is writing on his behalf. Martin gives a very detailed description of McKendree\u27s afflictions. The Bishop says he enjoyed Finley\u27s letter and wishes to be remembered to all his old friends. Abstract Number - 135https://digitalcommons.owu.edu/finley-letters/1133/thumbnail.jp

US GAAP Conversion To IFRS: A Case Study Of The Balance Sheet

International Reporting Standards (IFRS) has become the required framework for most of the world financial market economies. In the United States, US Generally Accepted Accounting Principles (GAAP) is still required. However, plans are presently in place by the SEC to abandon US GAAP and to adhere to IFRS requirements by as early as the period ending December 31, 2014. This case study requires the student to transform a US GAAP presented Balance Sheet to IFRS and is most suitable for an Intermediary Accounting 11 and a Financial Analysis class at the graduate level

US GAAP Conversion To IFRS: A Case Study Of The Income Statement

International Reporting Standards (IFRS) has become the required framework for most of the world financial market economies as of January 1, 2011. In the United States, US Generally Accepted Accounting Principles (GAAP) is still required. However, plans are presently in place by the SEC to abandon US GAAP and to adhere to IFRS requirements by as early as for the period ending December 31, 2014. This case study requires the student to transform a US GAAP presented Income Statement to IFRS. This case study is most suitable for an Intermediary Accounting or a Financial Analysis class at the graduate level

Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study

Mobile digital therapeutics for autism spectrum disorder (ASD) often target emotion recognition and evocation, which is a challenge for children with ASD. While such mobile applications often use computer vision machine learning (ML) models to guide the adaptive nature of the digital intervention, a single model is usually deployed and applied to all children. Here, we explore the potential of model personalization, or training a single emotion recognition model per person, to improve the performance of these underlying emotion recognition models used to guide digital health therapies for children with ASD. We conducted experiments on the Emognition dataset, a video dataset of human subjects evoking a series of emotions. For a subset of 10 individuals in the dataset with a sufficient representation of at least two ground truth emotion labels, we trained a personalized version of three classical ML models on a set of 51 features extracted from each video frame. We measured the importance of each facial feature for all personalized models and observed differing ranked lists of top features across subjects, motivating the need for model personalization. We then compared the personalized models against a generalized model trained using data from all 10 participants. The mean F1-scores achieved by the personalized models were 90.48%, 92.66%, and 86.40%, respectively. By contrast, the mean F1-scores reached by non-personalized models trained on different human subjects and evaluated using the same test set were 88.55%, 91.78%, and 80.42%, respectively. The personalized models outperformed the generalized models for 7 out of 10 participants. PCA analyses on the remaining 3 participants revealed relatively facial configuration differences between emotion labels within each subject, suggesting that personalized ML will fail when the variation among data points within a subjects data is too low

The Effect of Business Development Services on Performance of Small and Medium Manufacturing Enterprises in Kenya

Small and Medium Enterprises have been regarded to play significant roles of job creation, poverty alleviation and economic development of many countries worldwide. These enterprises are however affected by many different factors. How these factors manifest singly or jointly is therefore a key concern for these organizations. Vital among these factors are business development services that affect how organizations produce and sell their goods and services. There is however a dearth of studies focusing on effects of aspects of business development services on organizational performance in Kenya. This study aimed at establishing how market access, procurement services and infrastructure facilities affect performance of small and medium manufacturing enterprises in Kenya. The study adopted a cross sectional survey design and examined primary data collected from 150 enterprises in Nairobi. Inferential statistics were used to interrogate relationships between independent variables and performance while descriptive statistics were used to determine distribution, central tendency and dispersion and hence establish conformity to linear regression requirements. Contrary to expectation, the results for market access did not show any relationship but procurement services and infrastructure facilities each had a positive and significant influence on performance of the enterprises. Furthermore, it was established that the joint effect of the three variables on performance of studied firms is greater than their individual effect. This study therefore concludes that, since procurement services and infrastructure facilities showed a positive influence on performance of small and medium manufacturing enterprises in Kenya, these enterprises should adopt strategies that enhance procurement and improve infrastructure facilities to experience better performance

The Planet, 1992, Volume 21, Issue 02

https://cedar.wwu.edu/planet/1007/thumbnail.jp

Irrational beliefs and stress levels: Evidence among orphaned students in Kenyan secondary schools

In the study reported on here we examined the relationship between irrational beliefs and stress levels among orphans in public secondary schools in Kenya. Rational Emotive Behaviour Theory was adopted. In the study we adopted a cross-sectional correlation research design. A sample size of 350 double orphaned students in secondary schools was obtained using stratified and simple random sampling techniques. The Irrational Belief Inventory and Perceived Stress Scale were used to collect data. The reliability results indicate the Cronbach’s alpha values ranging from 0.672 to 0.756. Quantitative data from questionnaires were analysed using inferential statistics such as Pearson correlation and regression analysis. The findings established a weak positive (n = 314, r = .149; p = .008 < .05) Pearson product-moment correlation coefficient between demandingness and stress levels; a weak positive (n =314, r = .243; p < .05) correlation between awfulizing and stress levels; a weak positive (n = 314, r = .191; p < .05) correlation between irrational belief for low frustration tolerance and stress levels; and a weak positive (n =314, r = .167; p = .003) correlation between irrational belief of worthlessness and stress levels. The implication of these findings is that orphaned students are overwhelmed with stress because of their state of irrational beliefs. It is recommended that school counsellors should train orphans in secondary schools on rational beliefs through therapy techniques such as positive self-talk to counter the irrational beliefs