Building An Authentic Man And Machine Conversation

The Internet of Things (IoT) is rapidly expanding. But as society continues to add devices, our interactions need to be as authentic and practical as possible. Talking at your gadgets is a far cry from talking to or with them. The first implies one-sided communication; the latter implies actual understanding and action. Therefore, a push has occurred in the field of speech recognition, to make voice control software as error-free as possible.

Take a company such as Wit.ai. Their focus is on streamlining voice control systems with internet-connected devices. By helping to develop custom voice systems for human and machine interactions, companies like Wit.ai are easing the building of these applications. Their programming language is also learning and adapting to human language during every interaction.

Just Look at the Device and See What it is Doing

As voice-controlled devices become more accurate, so too does the desire to only just use your voice to verify the task. If you must confirm voice instructions by looking at the screen, the novelty of speaking, becomes just that, a novelty—because you become pre-occupied with the method of action, not the result of the action.

The IoT network must function effortlessly: Adjustments are made on the spot during human interactions. Unless your device has complicated software to constantly perform calculations, it becomes no better than any other manually-operated piece of technology.

The Wit of Common Wisdom

Developers at Wit.ai are determining a variety of ways that a command can be written. The utterances of a person’s question are sent to Wit.ai’s servers. Wit.ai, in turn, sends instructions to the device that answers the question or performs the task. For smaller developers, Wit.ai serves as a strong starting point for voice systems.

Many newer device designs are simply removing screens and buttons. This makes voice control even more important. Speech unifies the many IoT devices that are out there. As consumers, we are also becoming more comfortable using our voice to activate apps, search for content, and ask questions. But with so many voice variations, intonations, inflections, etc. translation software must be just as innovative as the development of the electronics themselves. We need real and differentiated language.

This…is…Not…How…Humans…Talk…to…One…Another…

Voice systems need to be developed for complete flexibility and scalability. The data absorbed via voice is ever-changing. IBM has made some progress in this area, recently reaching a new record in speech recognition — only a 5.5% word error rate. For this most recent test, IBM’s SWITCHBOARD system utilized pre-arranged recorded human conversations. The SWITCHBOARD system has been the go-to method for over twenty years for processing speech recognition tasks.

Humans understand each other in countless ways. By trying to build software that can predict every single utterance, it is understandable that some data will be neglected. So instead of the machine searching through its database to find matches, it needs to learn from what it is taking in and respond appropriately. Speech recognition software is not yet at the level of human understanding, but it continues to improve. Society’s determination of understanding this must be governed by comparing human and machine performance using similar assessments. The closer these results become, the more fine-tuned speech recognition will become.

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