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What is the difference between automatic transcription and automatic speech recognition? Are they the same?
Is my following interpretation correct?
Automatic transcription: it converts the speech to text by looking at the whole spoken input
Automatic speech recognition: it converts the speech to text by looking into word by word choices
They are both the same. There are different algorithms to recognise speech, but essentially they all aim to identify the content of the spoken input and convert it into written text.
Automatic transcription is then done, whereas the output of more general ASR is often passed on to further processing, such as recognising entities or commands expressed in the speech.
automatic transcription is a system to change phonemes to graphemes automatically, it's more like syllabel recognition, used for build speech recognition in another language base on stable and existing version of speech recognition. see this picture to more understanding: see this picture
one think that you must understood, sound is different with meaning, computer need tools to understood the sound, it's speech or music or another form of sound. all speech sound already mapped in International Phonetic Alphabet (IPA).
and with little computation to combine the graphemes (call dictionary), resulting understanding speech of specific language.
example: you already know the sound of "spoon" or 'key', the grapheme is split by s and 'p-o-on'.
in other language like Indonesia's language, another developers use this grapheme identification system (transcription system) to build a speech recognition, for words 'meskipun'.
in grapheme, 'meskipun' words is: 'mehs-key-poon'.
with a simple computation, we can make computer understood the 'meskipun' words.
only said if "mesh + key + poon" show, the words is 'meskipun'
meskipun (=althought, english)
The big problem in building speech recognition with using automated transcription system, there is no 100% similar for IPA map in every language.
so the developers should use several 'transfer learning' database of language to make their speech recognition have more high accuracy. except, he decided to build it from scratch.
automated speech recognition is an end to end speech recognition that set to understood a specific speech language, which in the middle of the system contain automatic transcription system.
