From Speech to Text, Artificial Intelligence, to Text Mining–Data is ubiquitous and growing at an unprecedented rate, mathematically, upwards and onwards exponentially, without an apparent limitation. Contrary to popular understanding that data is just a concept discovered in our recent technological era, the history of humankind’s relationship with data stems back millennia. Data is simply about the concept of acquiring, understanding, and synthesizing information, in both measurable (i.e., numerical) and unmeasurable forms (e.g., observational). The measurable is digital or mathematical in nature because it can be manipulated or quantified. Undoubtedly, data has become the new currency for economies and corporations worldwide; it has also become the backbone of innovative breakthroughs in nearly every discipline and industry. Data drives strategy, discovery, and forecasting.
Before the digital frontier, mainframes, and programmable cards, information gathering through observation made its way to becoming numerical data by only the human eye, a writing utensil or knotted strings to the Quipu, and later, paper–or in Archimedes’ style and time (circa 2nd century BC), sand grains, mathematical formulas, and scaled mechanical models. In all, numerical data was not easily obtained or just inconceivable.
In today’s world, with an expanding Internet of Things (IoT) ecosystem, our constant state of being connected online with a multitude of devices from our wrists, computers, to our smart auto vehicles, digital data is accumulating without bound. However, the facilitators and makers of digital data, such as a software program, wearable, mobile device, mobile app, GPS-enabled device and more, does not do so perfectly. Generated digital data exist in streams including lots of questionable data and errors based on unintended inputs and systemic mistakes. For example, from video to recording, speech is a complex expression of spoken words and sound and may differ from one individual to another. A person may change speech several times–if not more–during a lifetime based on meeting new people of new geographical regions, differing dialects, new environments, adaptation, physical attributes, age, learning, and so on–all affecting and changing speech. Simultaneously, accompanying ambient sound is also highly complex and constantly changes. Not considering the hardware and microphone, speech recording devices capture audible sound and all its intricacies. However, to understand patterns–analytically, computationally or mathematically–speech and sound are often converted into measurable form resulting in meaningful and meaningless information.
From model design to purpose, if the objective is to analyze speech patterns, distracting sounds, conversion errors from when speech and sound were digitized, to unclear sound or inaudible speech that should be removed–this is what we call noisy data. Not to be confused with outliers, noisy data increases analytical error, because it is not necessarily discernable or understandable by either human beings or the machines processing it.
Dealing with noisy data is the lever to any desired breakthrough; the better the data, the more accurate data models are, and the more insights can be revealed. How is noise dealt with? Beyond data cleaning and preparation, the right data quality assurance (QA) reviews and filters data systemically based on key parameters and purpose; it’s an ongoing exercise that requires human expertise alongside machine power. Athreon’s QA models are methodical and diligent. With data increasingly growing and noisy data increasing in direct proportion–if not more–the path to successful academic studies, scientific breakthroughs, insurance case audio processing, law enforcement case interview transcription, healthcare, medical data, intelligent models, and analytics, is data quality assurance wrangling noisy data.
Learn how Athreon can help you in your speech to text and quality assurance needs. Diminish the error margins. Contact Us Today!