![]() ![]() The speech data that’s transcribed for AI can come in the form of human-to-machine speech (e.g. In some cases, transcriptions can also include non-verbal sounds and background noises. The transcriber-either a person or a computer-records what is said, when it’s said, and by whom. Speech transcription for AI, specifically, is transcription that’s used in combination with speech recordings to train and validate voice recognition algorithms on a variety of applications-from voice assistants to customer service bots. There’s a distinction to be made between general-use-case transcription and transcription for AI. In this article, we’ll discuss why human transcription is still necessary in an increasingly automated world and why we take a consultancy approach to transcription for AI. Therefore, when you consider the unique (and sometimes strange) training data needs of today’s speech technology developers, a one-size-fits-all approach to speech transcription is destined for failure.īefore starting a transcription project, you have to consider your project factors such as use case, budget, quality requirements, necessary language expertise, and more. When clients come to us for speech data collection and transcription, they’re trying to solve for the edge cases where ASR still struggles-whether it’s recognizing a greater variety of accents or dealing with background noise. That’s because automatic speech recognition (ASR) already covers the simple transcription cases. ![]() But as a data provider to AI developers, the transcription projects on our plates today are anything but simple. Speech transcription is a simple task on the surface: write down what was said in an audio recording. But when your goal is to further improve the accuracy of automatic speech recognition, you’ll still need the help of real-life human transcribers. Automatic speech transcription has reached near-human accuracy levels at a fraction of the cost and effort.
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