TauRho Transcribes only uses human transcribers because human beings are better able to cope with the idiosyncrasies of human speech. Machine transcription has, however, grown over recent years as market researchers have decided to try out the software options available. And indeed, in certain cases Automatic Speech Recognition (ASR) software can produce adequately accurate content from which it is possible to take reasonably reliable findings. But to achieve this, good quality, clear audio combined with distinct and well-defined speech from just one or two speakers is ideal. If the audio for transcription is imperfect ASR software cannot yet promise the consistency and reliability which the human ear provides. So ASR cannot be assumed to be an accurate substitute for human transcription.
Adding in the slightest complexity such as additional speakers can affect the quality. Other variables including accents or dialects, interruptions, muffled or cloudier recordings, can quickly lead to distortions which result in the transcript presenting a minefield of unreliable findings. So, if you are looking for consistently reliable findings for your research, we recommend a good human-resourced transcription service.
Of course, ASR tools are improving and some do offer a reasonable option when combined with good quality post-editing services, but finding such a combination can be challenging given the number of players and no clear market leader. If opting for an ASR tool, in order to save time and trouble it is advisable to use a reliable, human proof-reader. Proofreaders at TauRho Transcribes of London are accurate and dependable. Proofreaders at TauRho are first and foremost trained as transcribers so tackling post-editing machine transcription is straightforward. They are familiar with a whole range of voice variables, multiple speakers and overlapping speech, so deciphering ASR imperfections is well within their remit.
The need for proof reading by a trained ear is all the more important in the case of technical transcriptions. Medical and automotive transcriptions, for example, are often littered with technical jargon which is critical to the meaning of the text, and this can be a major challenge for Automatic Speech Recognition software. In these instances, whilst post-editing machine transcription is an option it may be neither efficient nor cost effective, in which case human transcription from the outset is the better option.
