COMMENTARY
a cliff) or shouldn’t go (off a cliff) and requires a specific sensor array to implement. A system should also understand a user’s specific preferences, style of speech, language and accent. Te abil- ity to recognize and adapt to a user, though more complex, means less training is required on the user’s part to adapt to the agent.
Te third use case for automatic speech recognition, context analysis of voice communications, can be used for intelligence gathering, but also for maintaining situational awareness for our own forces—for example, detecting when Soldiers talking over the radio mention an enemy tank or incoming fire. Speaker detection is an important automatic speech recognition tech- nique to build situational understanding—for example, being able to distinguish between participants in a conversation, a feature, called "speaker diarisation," informs inferences about the relationship between the participants (i.e., different ranks and roles) and how that bears on the content of the discussion. For radio communications, speaker detection is also key to threading together conversations that may be coming through an operations center as a series of separate voice data packets.
Te Army Rapid Capabilities and Critical Technologies Office (RCCTO) sponsors the Virtual Assistant for Mission Operations (VAMO) project. Working with the Massachusetts Institute of Technology Lincoln Labs, a federally funded research and devel- opment center, VAMO is exploring the use of text and speech interfaces to provide transcripts, autofill forms and summarize content. Te VAMO team recently participated in a technical exchange with the Navy, which is working on a similar project, the Ambient Intelligence Speech Interface (AISI).
HEARING THROUGH THE NOISE While we’re already starting to deploy automatic speech recognition-enabled systems, research challenges are still being addressed by our science and technology community to expand capabilities and improve outcomes. One of the most needed resources in this development is not expensive—it’s data. Machine learning approaches are promising, but require large quantities of data. Talk via radio, in command posts and in moving vehicles— both during routine operations and during engagements—are necessary to ensure that the models are both sensitive to changes like noise levels or phase shifts, and also robust enough to handle natural variety.
Noise is one of the main factors that differentiates the results of battlefield tech from its commercial equivalents. It’s also not likely that a single solution will fix this problem. Better micro- phones may help, as will custom noise filters, denoisification
NOW WE’RE TALKING
Much work has been done in the field of natural language processing, but it’s important to note that written communication can be very different from spoken communication. Spoken communication is often less formal, because it can afford to be so. Take, for example, the following dialogue, which could take place between two humans or between a human and a robot interface.
Initiator: “Get that thing over there.” Respondent: “Where?” Initiator: (Points at object.) Respondent: “The blue one?” Initiator: “Yes, the blue one.”
There are a few things happening in this dialogue. The initial command, “get that thing over there,” is ambig- uous. Neither what the thing is nor where are precisely defined. The following lines go back and forth rapidly to clarify the request. The initiator indicates a location by pointing and the other party identifies the object of interest by asking if it’s the blue one.
One thing that speech allows for is a quick exchange. Had the original request been in writing, the initiator would have been aware that the request was vague without sufficient context and might have written some- thing like “retrieve the blue box from the northeast corner.” Otherwise, the back-and-forth of the written messages would have taken too long. There’s also a question of economy of effort—a speaker wants to use as few words as possible, while the listener needs the words to be precise. Often, a speaker can also rely on additional channels of communication to augment the content—for example, intonation, pauses, volume, expression or gestures.
The medium of our communication affects the content. It’s a form of code-switching, where a speaker changes how they speak based on the audience. We could teach our Soldiers to speak in a manner that is more readily parsed by a computer agent, effectively forcing them to code switch, depending on whether they’re talking to man or machine. But let’s face it—if they take our language, the robots have already won.
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