One of the standard features of Interactive Voice Response (IVR) systems is known in the industry as "barge in." This is the capability that accepts your saying "billing" before the system gets through its spiel of "Please say 'billing' for Billing, 'technical support' for Technical Support, 'new accounts' for New Accounts, ..."
We have begun working on adding speech recognition to our Aloud line of products, so that you can dictate replies and control the app strictly through the use of your voice. Of course Barge-In is essential here: if, for instance, Friends Aloud is reading a long post that you're not interested in listening to, you should be able to quickly say something like "Next" and have it immediately stop reading that post and start reading the next one. Same with messages and emails and tweets and news articles. The whole idea behind the Aloud series is that they shine when you can't divert your eyes and hands from the primary task at hand.
But there's a technical problem that we must solve before adding this sort of obvious capability to our apps: how can the apps' speech recognition feature distinguish between the audio coming out of the mobile device's speaker vs. what the user says? In a telephone IVR system, the microphone generally does not pick up what comes out the handset earpiece, but in our case, most users are letting their mobile device speak to them using its built-in loudspeaker while both their eyes and hands are occupied. So we have a conundrum -- if the word "Next" is heard while the app is speaking, who said it anyway?
We are presently conducting testing of a variety of speech recognition technologies to see whether or not there exists one or more that can handle this problem. Some systems, for instance, claim to be able to identify human speakers based on their voiceprints. Perhaps we can use this sort of technology to distinguish between our app's voice and its user's voice? Alternatively, if the phone's microphone is directional enough and/or the audio coming out of the speaker quiet enough, we might be able to distinguish simply according to sound power level at the microphone input.
Bottom line: we hope and expect to add speech input and control to our apps within months. Stay tuned to this blog, where we will update you on our progress towards incorporating this powerful feature into the Aloud series.
We have begun working on adding speech recognition to our Aloud line of products, so that you can dictate replies and control the app strictly through the use of your voice. Of course Barge-In is essential here: if, for instance, Friends Aloud is reading a long post that you're not interested in listening to, you should be able to quickly say something like "Next" and have it immediately stop reading that post and start reading the next one. Same with messages and emails and tweets and news articles. The whole idea behind the Aloud series is that they shine when you can't divert your eyes and hands from the primary task at hand.
But there's a technical problem that we must solve before adding this sort of obvious capability to our apps: how can the apps' speech recognition feature distinguish between the audio coming out of the mobile device's speaker vs. what the user says? In a telephone IVR system, the microphone generally does not pick up what comes out the handset earpiece, but in our case, most users are letting their mobile device speak to them using its built-in loudspeaker while both their eyes and hands are occupied. So we have a conundrum -- if the word "Next" is heard while the app is speaking, who said it anyway?
We are presently conducting testing of a variety of speech recognition technologies to see whether or not there exists one or more that can handle this problem. Some systems, for instance, claim to be able to identify human speakers based on their voiceprints. Perhaps we can use this sort of technology to distinguish between our app's voice and its user's voice? Alternatively, if the phone's microphone is directional enough and/or the audio coming out of the speaker quiet enough, we might be able to distinguish simply according to sound power level at the microphone input.
Bottom line: we hope and expect to add speech input and control to our apps within months. Stay tuned to this blog, where we will update you on our progress towards incorporating this powerful feature into the Aloud series.
RSS Feed