• Team Voyc.ai

Fast. Accurate. Intuitive. Speech-to-Text is Evolving Transcription for a Better Research Experience

Updated: Jul 10, 2020

What do Research & Development employees, QA Analysts, Market Researchers and Social Sciences Academics have in common? This sounds like the setup for a marketing joke, but we’re talking of course about the act of transcription. Qualitative research methods are progressively being adopted across disciplines–it presents, after all, a more conscious and strategic approach to understanding people and data.

During the qualitative research project, a significant period of time and effort is allotted to the act of transcribing recorded data for analysis. Often cited or dismissed as an administrative formality, the act of transcribing voice to text is a necessity that won’t soon lose its value.

Manual Transcription Methods

Transcription can be done in several different ways, from hiring a transcription agency to (more recently) using audio-to-text converters. Many who are tasked with analysing recorded interviews will outsource this task to professional transcriptionists in order to help alleviate the burden. While this seems like the most efficient course of action, it presents a costly financial implication–on average $1.25 to $4.50 per recorded minute.

As for the turnaround time: depending on several factors such as recording quality, voice clarity and technical terminology–the industry standard for a one hour recording is a minimum of 4 hours to transcribe. Of course, many researchers prefer to do their own transcriptions, not only due to the cost implications or turnaround times, but simply because they are more confident in their own accuracy. This is especially true if they’ve conducted the discovery interviews and have a higher-stake in the research project to begin with.

Human VS. Machine (Learning)

People are particularly resourceful in the face of monotony. Finding ways around doing boring tasks is possibly the most common catalyst in every breakthrough innovation of our time. And one such innovation is the development of Speech Recognition or Speech-to-text technology.

Humans inherently affix meaning to written and verbal content; we resolve grammatical issues, spelling mistakes and understand nuances in speech, but this doesn’t come naturally to a machine. Language tasks such as transcription and translation have been slower to automate due to technical challenges, but this is all changing as a result of recent developments in machine learning.

Automated transcription has come a long way, with efforts dating back to the 1960s, but only since the mid-90s has this application been commercially viable. The marriage of artificial intelligence, computational linguistics and computer science has given rise to this branch of AI that empowers machines to “read” text by simulating the understanding of human language. This incredible field has effectively solved one of the most challenging problems in computer science and its accomplishments are part of our daily lives–whether we’re aware of it or not.

How Speech Recognition Empowers Researchers

When researchers are involved in qualitative text analysis, they must read through the data in order to assign codes and acknowledge commonly occurring themes for the continuous discovery of actionable insights. Another dilemma facing adherents of qualitative research methods is that due to time constraints and additional responsibilities, they are limited in the number of individual samples that can be analysed. Speech recognition has introduced the power to automate a large chunk of this process, effectively enabling marketers, researchers and QA managers to quickly and accurately transcribe recorded interviews.

Earlier we mentioned the turnaround time for manual transcriptions; by utilising the advances in speech recognition, that timeline is effectively sliced in half. The potential use-cases in adapting speech-to-text technology doesn’t end there. In fact, other uses include sentiment analysis, topic extraction, content classification and text summarisation. As you can see, it isn’t merely about transcribing voice to text, but about employing and adapting innovations in the expanding field of machine learning.

This intersection between technology and research is where Voyc thrives. By leveraging AI driven analysis and reporting, we help you along the research process so that you can easily find patterns and themes in your data.

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