If you're trying to subdivide color space into n colors, where n colors are as "psychophysically" as separate as possible:
Save most of the channel for brightness - our eyes can distinguish brightness/greyscale much better than hue or saturation.
The psychophysical conversion for RGB to grayscale is.55*G +.33*R +.12* B - this means that green is twice as bright as red which is three times as bright as blue. It also means you should carve up your color sampling space similarly.
Best psychophysical color space division I've ever seen is Apple's 256 color picker palette.
Hello everyone.
I'm Ashok, the CTO and cofounder of TuVox - the one with the Frankenstein green skin in the New York times article;-)
It's really great to see fellow slashdotters interested in our technology.
Some comments/thoughts/observations to offer. I'll try and add notes over the course of today.
We provide automated technical support using speech recognition as an underlying modality. Speech-based technical support is a very different kind of problem than a more conventional speech application.
Most speech applications are "few turns" and low ambiguity. It only takes a few interactions with the system to get a train schedule, or a stock quote and there is little ambiguity - you either want to go to City A, or City B. Companies that provide few-turn, low-ambiguity applications spend literally tens of thousands of dollars (or even hundreds of thousands of dollars), getting each turn to be as accurate as possible. The content of such an application rarely changes, and if it does, the rollout/testing period can be very long. A final note - these callers generally use the system frequently (ie. calling for a stock quote). Because of this, callers are willing to be educated on the commands/VUI to drive the system.
We, on the other hand, have to deal with long conversations (10 minutes), with users fumbling with their equipment, confused and angry, etc. You can imagine a call System: "How can we help" - Caller - "My $#@% machine doesnt work".... We have to get the caller to their answer, in spite of the fact that they don't know what the answer is. Additionally this is probably the first time the user has ever used the system. Finally, we have to make literally thousands of answers available in a conversational style, the day the product ships. That's when the highest call volume occurs (in the few weeks after the product ships). Oh...by the way - we use real humans for the voices, not text-to-speech. That makes the production schedule even more interesting!
Callers can leave us messages about their experience. It's really heartwarming (in the words of one of our customers) to hear what callers say - we got a call a few nights ago (at 1 in the morning) where a caller said he was glad that he was able to solve his battery recharging problem, because he thought he was going to lose all his data. The part of the New York Times article talking about people saying thank you occurs very frequently. They say thank you in so many places we have started to put thank you responses into the system.
Callers dont' have to wait. Callers get answers at any time. Callers dont get rude, untrained, agents abusing them (everyone at TuVox has had a horrible experience with an ISP tech support agent)! Our customers like that proposition!
Last point - It's not a choice between a live agent and automated support. We're offering the alternative to no agent at all. People think we're replacing agents. We're not. Our technology is designed to work with and support a tech support agent. Right now, our initial rollouts with customers are after hours because that's where the call volume is lowest and where we can fix any unforeseen problems. But there's even more interesting technologies in our pipeline.
Kindest Regards,
Ashok
Slashdot Mirror
If you're trying to subdivide color space into n colors, where n colors are as "psychophysically" as separate as possible: Save most of the channel for brightness - our eyes can distinguish brightness/greyscale much better than hue or saturation. The psychophysical conversion for RGB to grayscale is .55*G + .33*R +.12* B - this means that green is twice as bright as red which is three times as bright as blue. It also means you should carve up your color sampling space similarly.
Best psychophysical color space division I've ever seen is Apple's 256 color picker palette.
Hello everyone. I'm Ashok, the CTO and cofounder of TuVox - the one with the Frankenstein green skin in the New York times article ;-)
It's really great to see fellow slashdotters interested in our technology.
Some comments/thoughts/observations to offer. I'll try and add notes over the course of today.
We provide automated technical support using speech recognition as an underlying modality. Speech-based technical support is a very different kind of problem than a more conventional speech application.
Most speech applications are "few turns" and low ambiguity. It only takes a few interactions with the system to get a train schedule, or a stock quote and there is little ambiguity - you either want to go to City A, or City B. Companies that provide few-turn, low-ambiguity applications spend literally tens of thousands of dollars (or even hundreds of thousands of dollars), getting each turn to be as accurate as possible. The content of such an application rarely changes, and if it does, the rollout/testing period can be very long. A final note - these callers generally use the system frequently (ie. calling for a stock quote). Because of this, callers are willing to be educated on the commands/VUI to drive the system.
We, on the other hand, have to deal with long conversations (10 minutes), with users fumbling with their equipment, confused and angry, etc. You can imagine a call System: "How can we help" - Caller - "My $#@% machine doesnt work".... We have to get the caller to their answer, in spite of the fact that they don't know what the answer is. Additionally this is probably the first time the user has ever used the system. Finally, we have to make literally thousands of answers available in a conversational style, the day the product ships. That's when the highest call volume occurs (in the few weeks after the product ships). Oh...by the way - we use real humans for the voices, not text-to-speech. That makes the production schedule even more interesting!
Callers can leave us messages about their experience. It's really heartwarming (in the words of one of our customers) to hear what callers say - we got a call a few nights ago (at 1 in the morning) where a caller said he was glad that he was able to solve his battery recharging problem, because he thought he was going to lose all his data. The part of the New York Times article talking about people saying thank you occurs very frequently. They say thank you in so many places we have started to put thank you responses into the system.
Callers dont' have to wait. Callers get answers at any time. Callers dont get rude, untrained, agents abusing them (everyone at TuVox has had a horrible experience with an ISP tech support agent)! Our customers like that proposition!
Last point - It's not a choice between a live agent and automated support. We're offering the alternative to no agent at all. People think we're replacing agents. We're not. Our technology is designed to work with and support a tech support agent. Right now, our initial rollouts with customers are after hours because that's where the call volume is lowest and where we can fix any unforeseen problems. But there's even more interesting technologies in our pipeline.
Kindest Regards,
Ashok