But what are the opportunities surrounding voice in testing environments, and on the flip side, what are the challenges companies must look to overcome when looking to get the most out of voice recognition from a testing perspective?
AI, machine learning, and analytics are most effective when guided by human experience. In this sense, there’s no point investing time and resources into voice recognition software if this software is not seamlessly connected to your advanced analytics framework. If you’ve got AI and machine learning models in place, it’s time to consider connecting a voice-enabled automated testing system to this framework.
If you’ve ever waited for a product manager or analyst to write up hypotheses or use cases for testing purposes, you’ll know how long it takes. With voice-enabled testing, your product and market experts can guide your testing products via voice rather than by keyboard and mouse commands. This means that they are effectively able to direct your advanced analytic models by verbally proposing hypotheses and suggesting focus areas.
When all of this is possible through using verbal explanation rather than keyboard communication, experts can explain their commands more naturally.
Voice recognition as a technology is relatively simple; the real challenge is that by giving computers more human interaction mechanisms, people expect them to behave far more like humans, with all the flexibility and understanding this implies. For example, when individuals buy a product on a website, they fill in the boxes with the relevant information. However, when using voice recognition, users don’t provide information in order, and so the software can become confused.
In this way, the biggest challenge in integrating voice recognition capacity into the testing process is accommodating the shift of responsibility for making the human-machine interface work. Traditionally, humans have had to learn how to make most enterprise IT systems work, and this is usually carried out through the extensive use of instruction manuals or tutorials, alongside training.
With voice recognition software, the ease of which humans can interact with computing is greatly improved. However, people often begin to assume the machine ‘understands’ their verbal commands and will automatically adapt itself to the dialect and complexity of the expert’s unique style of testing. Companies may also start to expect AI to analyse with far more flexibility and to develop an understanding of multifaceted ideas than it is currently incapable of. If these challenges occur, the final user experience will be considerably substandard.
With an increased acceptance of voice recognition software, today’s testing environment is likely to continue to become increasingly voice-enabled to allow product and market experts to create the voice-centric tech of tomorrow. Driven by products like Siri and Amazon Alexa, there will be an acceleration in the deployment of smart home devices. With voice-controlled products like the Echo Dot coming to market we can expect to see a lot more voice-controlled products in areas as diverse as lights, heating, TVs, and kettles in the year ahead.
Alongside this, in his book ‘The Last Lingua Franca’, Nicholas Ostler predicted that while many people have projected that everyone on Earth will be speaking a mix of English, Spanish, and Chinese in 80-100 years, recent trends have shown an increase in regional dialects. Technology (in particular a ubiquitous, fast and accurate translation) will mean that a common language will no longer be needed. Accurate voice recognition, natural language processing, and translation are making this prediction look more and more likely to become a reality.
With voice-controlled products sitting firmly on the top of the consumer tech agenda, testing voice recognition will need to become an integral part of the testing process if these ambitions, and in some cases expectations, are to be met. Of course, there are still plenty of challenges in the way of taking full advantage of AI, machine learning and analytics, as well as making voice recognition a larger part of the testing process. However, once AI overcomes the major challenges of processing complex language quickly and accurately, it will be an integral part of the testing process.
Antony Edwards is CTO, Testplant
