Even with advancements in speech recognition software, human transcriptionists still provide the most accurate transcription services possible. Commercially available speech recognition software commonly has an error rate of nearly 12% when transcribing conversational speech over the telephone, compared to an average human transcriptionist error rate of 4% and the GMR Transcriptionist error rate of 1%. If you need accurate and professional quality transcription services, save yourself the risk and use 100% human transcription with GMR Transcription.
The largest and smartest artificial intelligence companies in the world (including Google, IBM, and Microsoft) have been working for years on improving automated speech-to-text transcription but still can only get up to 88% accuracy at best. GMR Transcription provides 99-100% accuracy by utilizing human transcriptionists.
The biggest advantage human transcriptionists have over automated transcription software is their natural ability to filter through background noise. Of course, presenting the clearest quality audio file is always the best practice. However, humans can filter through background noise and still deliver an accurate transcript. Automated transcription services have a difficult time handling background noise. This results in inaccurate transcripts or even a complete rejection of the audio file.
Another obstacle for automated transcription software is accents and dialects. When there are varying accents, or even a dialect the software cannot recognize, speech recognition software struggles. Humans, however, are continually exposed to varying accents and dialects. This exposure paired with human transcriptionists’ natural ability to adapt give humans the advantage – no matter the speaker’s accent. In an increasingly global marketplace, GMR Transcription is proud to offer high quality transcription services on audios with varying accents.
One of the quickest ways to tell if a transcript was done by a machine is to look for errors in homophones. Homophones are two or more words that have the same pronunciation but different meanings, origins, or spelling. Speech recognition software must rely on sentence structure to predict the most probable word to use. This can often lead to mistakes. For example, let’s say that a doctor was frustrated and lost his patience with someone. A human transcriptionist would transcribe, “The doctor lost his patience,” based on the context. An automated transcriber would likely transcribe, “The doctor lost his patients.”
Automated transcription software typically cannot identify individual speakers and deliver accurate transcripts when there are more than 2 speakers present. This can prove very troublesome for anyone who needs to transcribe audio with 3+ speakers, such as a business meeting. Humans, however, can understand and identify multiple speakers, even when the speakers sound alike. Not only will automated transcription software struggle to identify multiple speakers in the first place, but it also cannot accurately track those speakers throughout the audio.
With these pitfalls, and many more that automated transcription software face, many services have adopted a hybrid model of transcription. The process requires a human to edit and proofread a transcript after running it through speech recognition software. This model is done to cut corners and reduce lead time. However, results are often less accurate because the transcript created by the automated transcription software can vary drastically from the actual audio.