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The Impact Of Speech Recognition In The Transcription Industry

<p style&equals;"text-align&colon; justify">Transcription is one field that has come a long way in terms of development&period; One of the most recent trends shaping this industry is the use of speech recognition technology &lpar;SRT&rpar;&period; With the use of software&comma; it is now possible to create transcripts without having to transcribe audio files manually&period; These applications work by matching sounds waves with an inbuilt database of words&period; So&comma; when an audio file plays&comma; the software does all the typing&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify">Many software engineers have created speech recognition applications that can transcribe with high accuracy&period; So&comma; what does this mean for the transcription industry&quest; Will these programs phase out the need for transcribers&quest; How plausible is the technology that they use&quest; Let’s have a look at the positive and negative aspects of SRT with respect to the transcription industry&period;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>How Voice Recognition Technology Benefits the Transcription Industry<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify">The use of speech recognition software has brought several positive changes that not only benefit consumers&comma; but also audio typists themselves&period; These benefits include&colon;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Sustained Gains in Productivity<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify"><strong> <&sol;strong>With speech recognition applications&comma; transcribers simply need to edit and perform quality assurance checks on software-generated transcripts&period; Since these programs type faster than even an adept typist&comma; this has resulted in an overall increase in productivity&period; 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&period;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Cost efficiency<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify">When transcribers can produce more without having to strain a lot or spend much time on an audio file&comma; transcriptions rates ultimately go down&period; That is what voice recognition applications do when deployed well&period; These applications cut overhead costs by reducing transcription staff&period; As such&comma; organizations are realizing that they can generate an increased volume of high quality transcripts with fewer resources by tapping into speech recognition technology&period;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Flaws that Plague SRT<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify">While SRT has several benefits&comma; it also has some drawbacks&period; The weaknesses facing this technology mainly revolve difficulty in decoding&colon;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Overlapping speech<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify"> Speech recognition programs perform exceptionally well when transcribing simple dictations that involve one speaker&period; However&comma; most systems have difficulty separating simultaneous speech&period; As a result&comma; trying to transcribe conversations or interviews where speakers frequently interrupt each other is likely going to result in an extremely poor quality draft transcript&period;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Homonyms<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify">Homophones or homonyms are words that you pronounce the same way but are different in meaning&period; A few examples are &OpenCurlyQuote;suit’ and &OpenCurlyQuote;suite’&comma; &OpenCurlyQuote;bare’ and &OpenCurlyQuote;bear’ or &OpenCurlyQuote;compliment’ and &OpenCurlyQuote;complement’&period; Speech recognition applications cannot tell the difference between homonyms since they rely on pronunciation of words&period; In such cases&comma; it is up to the listener to spot such errors and correct them based on the context of what was said&period;<&sol;p>&NewLine;<h3 style&equals;"text-align&colon; justify"><strong>Poor audio quality<&sol;strong><&sol;h3>&NewLine;<p style&equals;"text-align&colon; justify"> Applications using SRT need to hear the words spoken distinctly in order to be accurate&period; Any background noise reduces clarity of the audio file&comma; which in turn compromises on quality&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify">Despite these flaws&comma; speech recognition technology continues to gain usage&period; As this technology continues to improve&comma; it will have a much bigger positive impact in the transcription industry&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify">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&period; She is also a contributing writer for 1st Class secretarial services&comma; a leading audio typing agency in the UK&period;<&sol;p>&NewLine;

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