Speaker Consistency
Detect when your TTS provider silently changes the voice. Every file in a batch is compared against the rest - outliers are flagged automatically.
The problem
TTS providers sometimes change how a voice sounds between API calls - and they don't always tell you. The result is a batch of audio files where most sound fine, but a few sound like a completely different speaker.
A voice model gets updated server-side and suddenly some files in your batch sound subtly (or obviously) different. If you're generating hundreds of files for an audiobook or training dataset, even a small shift can break consistency.
Providers occasionally retire or replace voices without notice. You request the same voice ID and get back something that sounds different - but the API returns no error, so nothing looks wrong until a human listens.
Some providers route requests through different regions, and the same voice can sound different depending on which server handles it. Your batch ends up with inconsistent audio through no fault of your own.
Certain text patterns can cause a provider to silently fall back from a neural voice to a standard one mid-batch, producing files that sound noticeably flat or robotic compared to the rest.
What people are saying
"When using Amazon Polly with Neural voices, Amazon Polly will switch to the standard voice. The 1st and 3rd sentences are read using the Neural voice, while the second sentence is read using the Standard voice."
"I was using Azure Speech Studio and now her voice is completely different. The speech style is very different and the audio is less clear."
"In both US West and US East regions I get the wrong voice, but in the West Europe region I get the correct voice."
"I tried all of the other voices just in case it got switched due to some kind of glitch but none of them are the voice I was working with before."
How we detect it
We listen to every file in your batch and build a picture of what the "right" voice sounds like - then flag anything that doesn't match.
We extract a voice fingerprint from each file
Every audio file is analyzed to create a compact representation of the speaker's voice characteristics - things like tone, pitch patterns, and vocal texture.
We compare every file against the batch
Each file's voice fingerprint is compared to every other file in the batch. This produces a similarity score between each pair, and an overall score for each file.
We flag the outliers
Files whose similarity score falls below the threshold are flagged as anomalies. You control how strict the threshold is - tighter for audiobooks, looser for varied content.
What you get
Clear, actionable results for every file in your batch
Similarity matrix
See exactly how similar each file is to every other file in the batch at a glance.
Per-file scores
Each file gets a similarity score showing how closely it matches the rest of the batch.
Regeneration list
A ready-to-use list of exactly which files need to be regenerated - no guesswork.
Stop shipping inconsistent audio
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