Although PLS files are a great way to globally set the pronunciation of words, their primary failing is that they aren’t a lot of help where context matters in determining the correct pronunciation. Leave the pronunciation of heteronyms to chance, for example, and you’re invariably going to be disappointed by the result; the cases where context might not significantly influence comprehension (e.g., an English heteronym like “mobile”), are going to be dwarfed by the ones where it does.
By way of example, when talking about PLS files I mentioned bass the instrument and bass the fish as an example of how context influences pronunciation. Let’s take a look at this problem in practice now:
<p>The guitarist was playing a bass that was shaped like a bass.</p>
Human readers won’t have much of a struggle with this sentence, despite the contrived oddity of it. A guitarist is not going to be playing a fish shaped like a guitar, and it would be strange to note that the bass guitar is shaped like a bass guitar. From context you’re able to determine without much pause that we’re talking about someone playing a guitar shaped like a fish.
All good and simple. Now consider your reaction if, when listening to a synthetic speech engine pronounce the sentence, you heard both words pronounced the same way, which is the typical result. The process to correct the mistake takes you out of the flow of the narrative. You’re going to wonder why the guitar is shaped like a guitar, admit it.
Synthetic narration doesn’t afford you the same ease to move forward and back through the prose that visual reading does, as words are only announced as they’re voiced. The engine may be applying heuristic tests to attempt to better interpret the text for you behind the scenes, but you’re at its mercy. You can back up and listen to the word again to verify whether the engine said what you thought it did, but it’s an intrusive process that requires you to interact with the reading system. If you still can’t make sense of the word, you can have the reading system spell it out as a last resort, but now you’re train of thought is completely on understanding the word.
And this is an easy example. A blind reader used to synthetic speech engines would probably just keep listening past this sentence having made a quick assumption that the engine should have said something else, for example, but that’s not a justification for neglect. The problems only get more complex and less avoidable, no matter your familiarity. And that you’re asking your readers to compensate is a major red flag you’re not being accessible, as mispronunciations are not always easily overcome depending on the reader’s disability. It also doesn’t reflect well on your ebooks if readers turn to synthetic speech engines to help with pronunciation and find gibberish, as I touched on in the last section.