Following several conversations with customers and partners and due to specific online reports, we have noticed a pattern that deserves attention.
Understandably, some people are confused about the differences between the powerful voice technologies becoming prevalent in our professional and personal lives. Unfortunately, combined with privacy and legal concerns, this lack of understanding can feed fears about committing to the adoption and use of these high-tech devices and programs.
To clarify and promote solidarity, we have decided to devote a blog series to the underlying science, principles, applications, and processes behind mainstream and up and coming voice-based technologies. Relevant to a particular technology type, each blog will cover underlying terrains such as the explanation of terms, definitions, use cases, scientific underpinnings, metrics, leaders, data-ownership, misconceptions, and ethical concerns.
Examples of the types of voice-analysis technologies we will explore are those categorized as either risk assessment, assistant, authentication, biometric, emotion-detection, or profiling systems.
As a result of this blog series, each of you will better understand why all voice technologies and software programs can’t be lumped together in the same category.
After reviewing the different voice analytics platforms, you’ll appreciate why and how a person’s intentions differ from environmental influences. If you stay with us on this expository journey, you will learn how some technologies are used solely for health-screening purposes, while others have forensics underpinnings. You’ll comprehend the pros and cons between single and multiple cue approaches, and the differences between macro vs. micro-level signals.
Of course, we’ll also explain how our keystone technology, Clearspeed VerbalTM, fits into the voice-tech landscape and the value it brings.
As different voice analysis methodologies are described in our Science & Technology blog, please keep an open mind while engaging your critical-thinking skills. When we say, “Not all voice analytics technologies measure the same thing(s) or come to the same conclusions,” you will know the reasons.
You’ll also appreciate the voice-tech research advancements made in this era, and how today’s automated programs fill in the blind spots. Indeed, today’s computer programs elegantly reveal what scientists, philosophers, and poets have known for centuries:
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