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Compared with audio content, a text transcript is searchable, takes up less computer memory, and can be used as an alternate method of communication, such as for subtitles and closed captions. The definition of transcription "software", as compared with transcription "service", is that the former is sufficiently automated that a user can run ...
The transcriptionist can replay the audio several times in a transcription editor and type what he or she hears to manually transcribe files, or with speech recognition technology convert audio files to the text. The manual transcription can be accelerated using different transcription hot keys.
An audio conversion app (also known as an audio converter) transcodes one audio file format into another; for example, from FLAC into MP3. It may allow selection of encoding parameters for each of the output file to optimize its quality and size.
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text (STT).
Some audio conversion functions can be performed by software or by specialized hardware. For example, an audio transcoder converts from one compressed audio format to another (e.g., MP3 to AAC) by means of two audio codecs: One for decoding (uncompressing) the source and one for encoding (compressing) the destination file or stream.
To develop its speech transcription technology, the company says it combined deep machine learning using millions of hours of audio recordings, which were analyzed to train the software and improve the transcription capabilities. The company says that it uses proprietary algorithms to scour the web for these usable audio segments.
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