#voiceui

Sunny Swedish Summer Swimming & Selected Stickers 💎🌊🏊🏻‍♂️☀️💫🍀🐲Kämpa mer! 🎯Inspired by The Frontiers Conference and Almedalen, Planning our upcoming events and developing myself via lifelong learning 🌟✌🏻💪🏻Goals are not seen only in football matches⚽️ #GlobalGoals 💚 #sustainability #innovation #changetheworld #globaljams #cocreation #ixdalife #ixdd IceCream after swimming is yum😋🍦👀 Linkedin Learning missed me and offered me two months🙏🏻 I take it 👍🏻I also like YouTube Learning but exploring YouTube Premium and YouTube Music sounds fun to do some competitor analysis in music and streaming industries and also balance the lifestyle too 🎼🎬 #UI #UX #VoiceUI #CX #UCD #HCI #IxD #UR #intutive #iterativedesign #design🤔 #servicedesign #servicedesigndoing #designthinking #designsprint2 #designdoing💡 (at Medialogist)

From the executive summary: Pilot understanding of the automation’s capabilities, limitations, modes, and operating principles and techniques. The HF Team frequently heard about automation “surprises,” where the automation behaved in ways the flightcrew did not expect. “Why did it do that?” “What is it doing now?” and “What will it do next?” were common questions expressed by flightcrews from operational experience.

Question intonation

Abstract: In an online automatic speech recognition system, the role of the endpoint detector is to infer when a user has finished speaking a query. Accurate and low-latency endpoint detection is crucial for natural voice interaction. Classic voice activity detector (VAD) based approaches monitor the incoming audio and trigger when a sufficiently long pause is detected. Such approaches are typically limited due to their inability to distinguish between within and end-of-sentence pauses. In this paper, we propose an endpoint detection algorithm that is integrated with the speech recognition process, leveraging acoustic and language model information in order to distinguish between within and end-ofsentence pauses. Unlike other integrated approaches that are based on the highest-scoring active recognition hypothesis, the proposed algorithm computes the expected pause duration over all active hypotheses, which leads to a more reliable pause duration prediction. We show that our method achieves significantly higher accuracy and lower latency in a comparison to standard approaches for endpoint detection.

From the authors: We created a digital audio platform to covertly modify the emotional tone of participants’ voices while they talked toward happiness, sadness, or fear. Independent listeners perceived the transformations as natural examples of emotional speech, but the participants remained unaware of the manipulation, indicating that we are not continuously monitoring our own emotional signals. Instead, as a consequence of listening to their altered voices, the emotional state of the participants changed in congruence with the emotion portrayed. This result is the first evidence, to our knowledge, of peripheral feedback on emotional experience in the auditory domain. This finding is of great significance, because the mechanisms behind the production of vocal emotion are virtually unknown.