Transcription is one field that has come a long way in terms of development. One of the most recent trends shaping this industry is the use of speech recognition technology (SRT). With the use of software, it is now possible to create transcripts without having to transcribe audio files manually. These applications work by matching sounds waves with an inbuilt database of words. So, when an audio file plays, the software does all the typing.
Many software engineers have created speech recognition applications that can transcribe with high accuracy. So, what does this mean for the transcription industry? Will these programs phase out the need for transcribers? How plausible is the technology that they use? Let’s have a look at the positive and negative aspects of SRT with respect to the transcription industry.
How Voice Recognition Technology Benefits the Transcription Industry
The use of speech recognition software has brought several positive changes that not only benefit consumers, but also audio typists themselves. These benefits include:
Sustained Gains in Productivity
With speech recognition applications, transcribers simply need to edit and perform quality assurance checks on software-generated transcripts. Since these programs type faster than even an adept typist, this has resulted in an overall increase in productivity. Some hospitals have been able to decrease turnaround time for important medical reports by outsourcing transcription tasks to audio typing agencies that make use of speech recognition software.
When transcribers can produce more without having to strain a lot or spend much time on an audio file, transcriptions rates ultimately go down. That is what voice recognition applications do when deployed well. These applications cut overhead costs by reducing transcription staff. As such, organizations are realizing that they can generate an increased volume of high quality transcripts with fewer resources by tapping into speech recognition technology.
Flaws that Plague SRT
While SRT has several benefits, it also has some drawbacks. The weaknesses facing this technology mainly revolve difficulty in decoding:
Speech recognition programs perform exceptionally well when transcribing simple dictations that involve one speaker. However, most systems have difficulty separating simultaneous speech. As a result, trying to transcribe conversations or interviews where speakers frequently interrupt each other is likely going to result in an extremely poor quality draft transcript.
Homophones or homonyms are words that you pronounce the same way but are different in meaning. A few examples are ‘suit’ and ‘suite’, ‘bare’ and ‘bear’ or ‘compliment’ and ‘complement’. Speech recognition applications cannot tell the difference between homonyms since they rely on pronunciation of words. In such cases, it is up to the listener to spot such errors and correct them based on the context of what was said.
Poor audio quality
Applications using SRT need to hear the words spoken distinctly in order to be accurate. Any background noise reduces clarity of the audio file, which in turn compromises on quality.
Despite these flaws, speech recognition technology continues to gain usage. As this technology continues to improve, it will have a much bigger positive impact in the transcription industry.
Mariah Smith is a stay at home mom and full time transcriber that loves to stay on top of the latest trends affecting the transcription industry. She is also a contributing writer for 1st Class secretarial services, a leading audio typing agency in the UK.