Voice technology is transforming the digital revolution across industries from automotive to hospitality, retail, and much more. As conversational AI solutions proliferate, users expect to interact with these platforms in any context. For all of voice technology’s promise, it remains clumsy and still sometimes falls short of the “human standard.”
In use cases where we expect a person to be able to hear and understand speech, we inherently expect our voice assistants to be able to hear and understand us in the same conditions. Instead, many voice technologies still have difficulties with noisy environments and even more so with distinguishing different voices in a crowded room.
As voice AI platforms become more robust, their dependency on clean data becomes more important. Accuracy in extracting voice metadata like intent, speaker ID, emotion, and health markers are all dependent on capturing the cleanest voice signal possible. Every great voice experience starts with world-class signal processing.
If we’re going to unlock the potential of voice and all of its valuable data, we need solutions designed for the real world that can extract high-quality, machine-recognizable voice data. Join us for a presentation with leading voice partners and learn how they are evolving voice technology into the next generation and what that means for companies and brands with the foresight to take advantage.
Ken is a serial entrepreneur who has leveraged nearly 20 years of strategy, product development, and business management experience to build a company from IP he co-authored with Dr. Hamid Nawab, a renowned researcher, engineer (PhD, MIT ’82) and Professor of Electrical and Computer Engineering at Boston University. His company was the recipient of a National Science Foundation (NSF) Small Business Innovation Research (SBIR) grant to conduct research and development work on voice technologies whose performance in speech and speaker recognition does not significantly degrade due to the presence of interfering voices or environmental sounds.