4 ms·
A good compromise on distractions and 'more inputs' is voice-based UI. Honestly, car companies and OEMs must be focusing on better voice-based UIs for cars rath
by shas3 13y ago
A good compromise on distractions and 'more inputs' is voice-based UI. Honestly, car companies and OEMs must be focusing on better voice-based UIs for cars rather than distracting touch screens. The Sync system in Ford cars is a well-intentioned example that doesn't work that well.
- woobar 13y agoMy pet peeve with Sync is they disable navigation input via touch screen when car is moving. Even when a passenger tries to enter it. Instead they force the driver to use their clunky voice-recognition to do this. Which is much more distracting. But this is a minor quip compared to how MS implemented Sync Services. It's 2014 and they use a voice(!) line from a pre-registered cell phone to connect to their services.
- kolinko 13y agoThat's a very American way of thinking about things. I assume you're American (or British), because voice-based systems are nearly useless for non-native english speakers. I'm a fluent english speaker, but due to my non-american accent, and the fact that my language is not one of the top 10 used in the world I still cannot operate Siri (or other voice systems) properly, and the dictation feature is nearly useless to me.
- shas3 13y agoI'm a heavily accented non-American, non-British (though I can claim English is my native language since it's been my language of instruction since Kindergarten). Google's voice recognition system, and of late, Siri both work fine for me. Sync, iPhone 'voice control', etc. are next to useless. In certain cases, I've tried to adapt. For example, my immediate family, rather than have their names or what I call them in my other native language, I've named them using common English words. So I can just say "Call Dad", and the speech recognition system works. When I say that OEMs and auto-makers should work on improving voice recognition systems, I mean the entire processing system: sound acquisition, signal conditioning, and machine-learning based speech recognition.