Quality Assurance

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Data Quality Assurance Wrangles Noisy Data Spurring Innovative Breakthroughs

Data Quality Assurance Wrangles Noisy Data Spurring Innovative Breakthroughs


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!

Automated Transcription Services – What should you consider?

Automated Transcription Services – What should you consider?

Automated speech to text transcription services are starting to gain more attention. They offer rapid turnaround time at a low cost. This can be attractive to clients that have audio or video files that need to be transcribed. When you don’t have the time or expertise to type the recordings yourself, an automated transcription service seems like a logical solution. But what should you consider before you upload your files through a web portal for transcription?

Transcription Data Security

If your recordings contain sensitive patient data, criminal justice information or confidential research, it’s imperative to take time to vet the speech to text transcription company. For instance, have they taken into account HIPAA, CJIS, and GDPR? Are they willing to sign a Business Associate Agreement? Do their transcriptionists undergo background checks? Who will have access to your data? Will it be encrypted in transit and while at rest? If the transcription company doesn’t have qualified answers to these questions, proceed with caution. Data mishandling and security incidents could put you and other stakeholders at risk.

Speech to Text Accuracy

When you’re a reporter facing a tight deadline or a university with a large transcription project, uploading your video or audio files for processing through an automated speech recognition engine can seem like a great solution on the surface. But what about the output? How accurate will the final transcript be? Transcripts produced by human typists are often upwards of 99% accurate. However, automated speech recognition technology can’t match this level of accuracy. Although speech recognition technology has improved over the years, it still leaves much to be desired. For example, speech engines make contextual errors that human transcriptionists are professionally trained to avoid.

Transcript Editing Time

 If you decide to use an automated speech to text transcription company, and you receive back 25 or 100 or 1000 pages of material, will you have the time to review every line of text carefully? Will you realistically be able to spend the hours necessary to compare the computer-generated text against the original audio file? Depending on how busy you are, this may not be realistic. When using an automated transcription service, it’s essential to factor in additional time to review the transcripts because speech engines are prone to making errors.

While it’s natural to want things rapidly, inexpensively, and of excellent quality, automated transcription companies are unable to deliver on all three objectives. And, if security is a consideration for your data, using an automated transcription service may be too risky. You can undoubtedly get your documents quickly and at a bargain price with an automated transcription service, but you will still need to budget extra time to review the transcripts carefully to ensure accuracy. After all, if you depend on quality transcripts to inform your decision making, inaccurate data can lead to adverse outcomes.

About Athreon Transcription

Athreon’s speech to text transcription services combine advanced speech recognition technology with the expertise of professional human editors. Our hybrid approach to speech to text conversion and our stringent security and quality standards ensure that transcripts are produced quickly, accurately, and securely. For more information about Athreon’s transcription services, contact us at 800.935.0973.