Text-to-speech converts written text into spoken audio. Recent neural models produce voices that are hard to tell apart from human speech.
Why it matters: It is the brick that produces the sound, but it is not where the quality of a French reading is decided. Good engines are plentiful; what separates the results is what you feed them.
The trapJudging a tool on its engine rather than on its text preparation. A gorgeous voice that reads a date wrong is still unusable in production.
A neural voice is generated by a neural network trained on human speech. Its natural prosody makes it hard to distinguish from a real narrator.
Why it matters: It is what makes a reading sound human or mechanical, at equal timbre quality. Over a four-thousand-character article, prosody decides whether anyone reaches the end.
The trapIt depends on the punctuation of the source text. An article with no commas and fifty-word sentences will sound breathless whatever the engine.
Word-level timestamps map every word of the text to its exact position in the audio. They power highlighting, quotes and chapters.
Why it matters: It is what makes highlighting, accurate subtitles, shareable clips and search-inside-speech possible. One piece of data, four features.
The trapThe timestamps refer to the SPOKEN text, which is not the displayed text. Using them as-is makes everything built on them drift.
The step that turns written text into speakable text: numbers become words, acronyms are expanded or spelled out, abbreviations are written in full.
Why it matters: This is the real work, and it is done language by language. French has its own rules, and that is where engines go wrong, not on the timbre of the voice.
The trapAssuming a language model handles it correctly. A model that guesses can read the same sentence two different ways from one day to the next, with no way to correct it.
Everything in speech that is not the words themselves: rhythm, pauses, melody, intensity. It is what makes a reading sound human.
Why it matters: At equal timbre, prosody decides whether four thousand characters are heard to the end or abandoned at the third sentence.
The trapA two-sentence demo says nothing about prosody. Monotony appears with length: test on a whole article, not on a chosen excerpt.
The smallest sound unit that changes meaning in a language. It is the alphabet used to write a pronunciation when spelling is not enough.
Why it matters: It is what lets you fix a mispronounced town name ONCE, instead of respelling it phonetically in every article.
The trapRespelling a name phonetically inside the article fixes the sound and damages the text: it ships that way into subtitles, transcript and index. The fix belongs in the lexicon, not in the article.
An initialism is spelled out letter by letter (BBC), an acronym is read as a word (NATO). Nothing in the writing tells you which is which.
Why it matters: French news copy is full of them, and getting it wrong is immediately audible: one botched acronym discredits the whole reading.
The trapExpanding an acronym at every occurrence is as bad as mangling it. You expand on first mention if the readership does not know it, and never afterwards.
A server exposing a product's functions as tools a conversational assistant can call, with the rights of your own key.
Why it matters: It is what lets you say 'voice the last three sports articles' instead of opening a screen and clicking.
The trap'MCP compatible' does not say HOW MANY operations are exposed. Ask for the table: a server covering half the API will leave the assistant failing on the very action you wanted to automate.