Active Signal Technologies (United States)

There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test models for SER, published work reveals a wide range of assumptions and variety in its use that challenge reproducibility and generalization. Based on a critical review of the latest advances in SER using IEMOCAP as the use case, our work aims at two contributions: First, using an analysis of the recent literature, including assumptions made and metrics used therein, we provide a set of SER evaluation guidelines. Second, using recent publications with open-sourced implementations, we focus on reproducibility assessment in SER.

Conversational Agents (CAs) can be a proxy for disseminating information and providing support to the public, especially in times of crisis. CAs can scale to reach larger numbers of end-users than human operators, while they can offer information interactively and engagingly. In this work, we present Theano, a Greek-speaking virtual assistant for COVID-19. Theano presents users with COVID-19 statistics and facts and informs users about the best health practices as well as the latest COVID-19 related guidelines. Additionally, Theano provides support to end-users by helping them self-assess their symptoms and redirecting them to first-line health workers. The relevant, localized information that Theano provides, makes it a valuable tool for combating COVID-19 in Greece. Theano has already conversed with different users in more than 170 different conversations through a web interface as a chatbot and over the phone as a voice bot.

U.S. Army helicopters used to provide aeromedical transport are extremely noisy (105 dB). This prevents auscultation with current stethoscopes. Ten subjects used four different stethoscopes in noise at 70–100 dB in order to determine the detection threshold of a body sound. The stethoscopes differed in performance (p=0.001); the best being the acoustic, followed by the three electronic stethoscopes. The threshold of noise for the detection of heart and breath sounds was 80 dB and 70–75 dB, respectively. A comparison of a standard stethoscope and one modified with Communications Ear Plugs revealed a difference (p=0.05) at 70 and 80 dB that disappeared at higher noise levels, implying that it was not simple masking, but that noise was amplified after entering the sensing head. Despite the poorer performance of the electronic stethoscopes, the need for a gain of 30–35 dB indicated that this was the preferred route for development. A sensor with an acoustic impedance match close to body tissue, but poorly matched to air-coupled noise, was developed, and trials indicate that heart sounds can be heard at 105 dB. Development continues with the aim of reducing or eliminating the need for signal processing.