| Key | Action |
|---|---|
| K or space | Play / Pause |
| M | Mute / Unmute |
| C | Select next subtitles |
| A | Select next audio track |
| V | Show slide in full page or toggle automatic source change |
| left arrow | Seek 5s backward |
| right arrow | Seek 5s forward |
| shift + left arrow or J | Seek 10s backward |
| shift + right arrow or L | Seek 10s forward |
| control + left arrow | Seek 60s backward |
| control + right arrow | Seek 60s forward |
| shift + down arrow | Decrease volume |
| shift + up arrow | Increase volume |
| shift + comma | Decrease playback rate |
| shift + dot or shift + semicolon | Increase playback rate |
| end | Seek to end |
| beginning | Seek to beginning |
Voice Assistants getting more and more popular and change the way people interact. Furthermore, more people get in contact with them and it is usual to use them in the daily routine. But, unfortunately most systems are still just voice-controlled remotes and the conversations still feels uncomfortable. Especially as the conversation activation needs a wake-word, which is still error-prone. This talk firstly discusses examples about errors in the conversation initiation and depicts the state-of the art in the research field of addressee-detection with a special focus on prosodic differences in the addressee behavior. Afterwards own analyses to the addressee behavior for modern voice-assistants in two different settings: a) interactions with Amazon's Alexa in a lab setting, dataset of similar dialog complexity between HHI and HCI. Subsequently, analyses of self-reports and annotator feedback on the speaking behavior will be discussed, followed by an overview of different recognition experiments to finally build an (intelligent) addressee-detection framework based on prosodic characteristics. The talk is then concluded by mention possible future research directions and open issues in experimental conditions.