I'm exploring synthetic voice data to improve an in-house speech recognition model. Real data is expensive and messy. Resemble's API looks promising for generating varied training samples.
Has anyone actually used it for this? I'm skeptical about synthetic data quality for ML training.
* What was the actual word error rate impact compared to real data?
* How does the cost of generating, say, 1000 hours of speech compare to collection/cleaning?
* Did you have to do extra preprocessing to make the synthetic audio work in your pipeline?
Looking for concrete benchmarks, not just "it sounds good."
Ask me about hidden egress costs.
I've run a similar experiment with a different vendor's synthetic speech for data augmentation on a keyword spotting model. The cost math is often favorable on paper, but the devil's in the implementation details.
For 1000 hours, vendor API costs could range from $5k to $15k, which beats professional recording sessions. However, our initial WER increased by about 18% when we substituted 30% of our real training data with synthetic. The synthetic samples lacked the subtle background noise and microphone artifacts present in our production environment, which made the model perform worse on real-world audio. We had to reintroduce noise floors and apply random gain adjustments to the synthetic audio to match our pipeline.
My advice is to run a small, controlled experiment first. Replace 10% of your training set with synthetic, measure the delta, and then model the full 1000-hour cost/benefit. Without that baseline, you're just spending optimization dollars on an unknown return.
every dollar counts
That "sounds good" is the whole trap. Those systems optimize for human perception, not for the acoustic oddities that actually help an ASR model generalize. You'll get pristine, unnaturally clean audio that teaches your model to expect pristine, unnaturally clean input.
The cost comparison is a red herring too. Sure, generating 1000 hours might be cheaper than collecting it. But if it degrades your WER, you've just paid to make your model worse. You'd be better off spending that budget on cleaning more of your existing messy data.
Everyone wants a magic data button. There isn't one.
Prove it
Exactly. The "pristine audio" issue is real. It's why I ask vendors about their acoustic parameter controls before a demo. Can you adjust background noise types and levels in the generation API, or are you just getting a clean studio voice every time?
If you can't simulate your actual deployment environment, you're building a model for a world that doesn't exist.
> We had to reintroduce noise floors and apply random gain adjustments to the synthetic audio
This is the key step, and it's a rabbit hole. We built a small script to profile the noise characteristics and average RMS of our real production audio, then applied those transforms in batch to the synthetic stuff. It helped, but we found we also had to vary the synthetic speaker's pitch and speed slightly to avoid an uncanny valley of uniformity.
Even with that, I'd second your advice to start with a 10% replacement experiment. The real cost isn't just the API call, it's the engineering time to make the synthetic data *un*synthetically messy.
Clean code, happy life
Used Resemble last year for an accent augmentation project. WER went up 15% on clean synthetic vs our real noisy dataset. Their API cost was around $8k for 1000 hours, which did beat manual collection.
But the cost/time hit came from post-processing. Had to build a pipeline to add:
* randomized room reverb
* microphone frequency response profiles
* slight sample rate jitter
Without that, the model overfitted to perfect audio. The raw synthetic data was useless. The real question is whether you have the bandwidth to engineer that noise layer yourself.
Ship it, but test it first