![]() Machine solutions tested by our company each year using general audio within the language and dialect offered by available machine audio to text converters still remains significantly low when it comes to accuracy. Machines, however, are still mostly trained for the single speaker (dictation). The human ear is designed to hear a lot of detail, especially in group conversations, noisy backgrounds, and a lot of other challenging acoustic situations. The accuracy of human transcribers is estimated to be around 94% – 99% depending on large variances in the quality of recording audio (see our recording guidelines for quality sound). Just consider the speed of speech, enunciation, pronunciation, slurring of speech, and swallowing of vowels per individual! It’s not surprising therefore that the accuracy level of transcription software for spoken languages, in general, is still lower than the ability of a trained and experienced human transcriber ear for multiple scenarios (2020).Īdd to that an excellent command of language, sharp hearing, and knowledge of the various subject matter and the relevant terminologies, which makes the context of any conversation also important, machine transcription solutions are still working at levels that can be largely disappointing as a single solution for general users if an accurate transcript is required. There are uncountable differences in the way people speak. Thousands of accents and dialects are spoken worldwide. Software developers have been working on machine transcription software that will convert audio files to text for many years. Another option to transcribe audio to text is Watson or IBM speech cloud, which offers a starting”lite plan”. Google Speech allows you to transcribe audio to text for good-quality recording but does cost you once you require a more specific output. Subsequently, some machine transcription providers push software which will “machine transcribe” your audio, while you edit alongside – essentially letting the machine convert audio file to text, while you correct the text.Ī good example of machine audio to text transcription is google audio to text, which typically converts mp3 to text (as well as other formats). ![]() These include a few machine transcription technologies that now prompt dialect choices (such as US English, British English, Australian English). There are increasing numbers of machine transcription solutions that offer “ audio file to text converters” in a range of languages. So does machine transcription really work? Another do-it-yourself but assisted solution.Īhhhh, the elixir of new technology, artificial intelligence (or AI to you techies), and all things virtual.
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