Re: [mst-content-hint] Differentiate between speech for human and machine consumption (#39)

Context defines 

> meant to be listened to by a human, and speech that is to be transcribed by a machine

The algorithm for the machine could include the requirement for lno modification of the signal, for the purpose of analyzing the applicable context itself. 

The machine does not care if there are artifacts included in the input or not. Those artifacts are part of the context, and can prove valuable, depending on the use case. 

The machine can only do what the human tells the machine to do. Since humans write code that runs machines, speech input or output is always meant for humans.

The human must always check the work of the machine, particularly when the domain is speech processing. 

The machine could output a recording of a human. A human could output audio through a machine. Would caution against attempting to differentiate between machine input and output and human input and output. It could to some group of humans sitting a conference room deciding what is human or machine input and output for everyone outside of that room, based on their subjective perspectives. 

The speech processing algorithm and in fact the consumer of speech input, rather human or machine, should only need to know that the domain is speech input, without attempting to perform some taxonomy upon the input, which, again, could lead to unintended consequences far beyond an API, while simultaneously using language in a specification: "They already defined that N output must be a machine and X output must be a human in their specification, must be a robot, a program, a machine, and not a human".

What are the observable differences between speech intended to be consumed by humans or machines?

Note, when lossy audio codecs are being used for the signal, the output is always from the machine, the lost context, which makes the output always lacking, being the ghost in the machine.

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Received on Wednesday, 8 April 2020 00:43:23 UTC