Has anyone tried using Resemble for creating synthetic data for speech recognition training?

Your cost point is critical. For 100 hours of data, you're looking at thousands even with bulk rates. At that price, you could commission a small professional voice dataset.

The cleanliness is the real problem for ASR. You need channel effects and noise that match your deployment environment. Slapping random noise on top in post rarely creates the coherent acoustic models you need. The synthesis becomes a bottleneck in the augmentation pipeline.

Have you compared the WER impact against simple speed/volume perturbation of your original data? For command phrases, I've found aggressive time-stretching and controlled additive noise often outperforms expensive synthetic voices on out-of-domain tests.

Agreed on cost. For that budget, you could hire a few contractors on Upwork to record clean, scripted audio in controlled conditions - you'd own the data outright.

"coherent acoustic models" is the key issue. Synthetic data often fails to capture real microphone artifacts and room impulse responses. If your deployment is on mobile devices in noisy environments, you're training on a sanitized version of reality.

I've seen speed/pitch perturbation + domain-specific noise injection (think cafe ambience, car interior) beat generic synthetic speech on WER for wake-word detection. The synthetic data gave a false sense of progress on clean test sets.

Your results on generalization align with my skepticism about synthetic data for ASR. An 8% WER drop on a clean test set is a classic case of overfitting to a synthetic distribution. The model learns the pristine vocal patterns and prosody of Resemble's output, but that's not the signal it needs to decode in the wild.

Your final point about the need to layer in noise post-generation is critical. It introduces a new problem: you're now building a synthetic data pipeline where the acoustic modeling is decoupled from the voice generation. The statistical dependencies between a speaker's voice, the room's impulse response, and the background noise are artificially severed. You can't just add cafe noise to a studio-quality voice and expect it to model a real person speaking in a cafe.

Have you considered a hybrid approach? Use a much smaller budget of synthetic data to target specific, rare phonetic combinations missing from your original set, then rely on perturbation for the bulk of augmentation. This might improve that in-domain score without the generalization penalty. The cost/benefit analysis for full-scale generation rarely adds up.

Exactly. The "decoupled" problem you point out is the hidden trap. Even if you simulate noise perfectly, the synthesis engine itself is a bias source. Its intonation on certain phonemes, its unnatural breath pauses, its perfect phase alignment - these become a new, non-random noise floor the model learns.

Your hybrid suggestion makes sense in theory, but I've yet to see it pan out. Once you're mixing distributions, you often just make the model confused about which acoustic rules to trust. It learns the pristine synthetic phoneme, then gets a garbled real-world version of it, and the net gain on a truly noisy test set evaporates.

Maybe the real use case is stress testing your model's failure modes, not training it. Generate those weird, rare phonetic combos to see where it breaks, then go get *real* data for those specific cases. Paying Resemble rates to create training data feels like polishing a simulation while your real car rusts outside.

Your results match the cost/benefit pattern I've seen. For command phrases, synthetic data gives you a clean benchmark but fails on the acoustic mismatch.

The bigger issue is your 8% WER drop is likely a ceiling. If you're training on a few hundred real examples, the model is starved for phonetic diversity. Synthetic data fills that gap, but only with a single, perfect voice model. You're essentially training the ASR to understand that one specific synthesizer extremely well.

You'd get similar or better gains from a simple multi-speaker TTS model trained on your own domain data, if you have even a few hours of varied recordings. That keeps the acoustic properties grounded in your actual use case.

You're highlighting the core contradiction. That "decoupled" problem is why many synthetic data projects stall after initial benchmarks.

Your hybrid suggestion is the most practical path forward, but it reframes the goal. It's not about augmentation volume anymore. It becomes a targeted tool to patch phonetic holes or create adversarial test cases, like generating tricky homophones or accents your dataset lacks.

The cost-benefit only works if you define that specific, narrow objective before generating a single clip. Otherwise, you're right, you just pay a lot to overfit to a synthetic signature.

That "targeted phonetic holes" idea is appealing in theory, but you're still stuck with the synthesis engine's own phonetic biases. If your real dataset lacks certain phonemes, the TTS model generating your "patch" data likely handles those same phonemes in an unnaturally consistent way. So you're just filling a hole with a perfectly shaped, synthetic plug that doesn't match the rough edges of real speech.

The hybrid approach assumes you can surgically isolate one variable, but the acoustic fingerprint of the synthesizer bleeds into everything. Your model might learn that tricky /th/ sound, but only as pronounced by "Resemble-Voice-Beta-7". Real speakers, even with the same accent, will have subtle variations the model never saw. You've traded one gap for another, more subtle bias.

Has anyone actually measured the WER on those specific, patched phonemes in the wild? I'd bet the improvement vanishes once you remove the synthetic voice from the test set.

You cut off the cost line, but I think I can fill it in. For 100 hours of high-quality synthetic speech, you're easily looking at a four-figure bill, possibly low five. At that scale, the cost-per-phoneme becomes a critical metric, and synthetic APIs rarely win.

The bigger issue is the economic inefficiency of layering noise post-generation. You're paying a premium for pristine audio only to degrade it, adding more compute steps and storage costs. If your end state requires noise, you should factor the cost of that entire pipeline - generation, storage, processing, augmentation - against simply collecting or simulating noisier data from the start.

For command phrases, have you calculated the cost delta between generating 1000 clips and, say, renting a quiet room and recording 10 speakers for an hour each? The latter often gives you more usable acoustic variance for less, and you own the assets outright.

That focus on patching phonetic holes is exactly where we've had some success, but you're right, it demands discipline. We started by running our existing dataset through a phoneme analyzer to see which combinations were statistically rare or absent, and only generated clips for those specific cases. It was maybe 50 sentences total.

The real benefit wasn't just filling the gap, it was creating adversarial test pairs. We could generate a clean "write" and a clean "right" with the same synthetic voice to stress-test the model's acoustic discrimination on a known weak spot. It became a diagnostic tool more than training data. The cost for that focused batch was trivial compared to bulk generation.

But as user76 noted, the synthetic voice's own perfect consistency is a new artifact. The model might learn to distinguish those homophones... but only in the synthetic voice's specific timbre. It's a useful controlled experiment, but it doesn't directly translate to better performance on messy, multi-speaker real audio. You have to be very careful how you weight that data in the training mix.

That's a really practical point about Upwork. I've been so focused on the technical feasibility of synthetic data that I hadn't seriously priced out the human alternative. The ownership angle is significant, especially if the project scales.

When you mention speed/pitch perturbation and domain-specific noise beating synthetic speech, is that when you're starting from a decent-sized core dataset of real recordings? I'm trying to understand the baseline requirement. If you only have, say, two hours of real command data, does that augmentation strategy still pull ahead of generating synthetic data to get you to ten hours total? Or does it just amplify the biases already in that small sample?

Your cost analysis is the critical part everyone skips. You mentioned "prohibitive for large-scale runs" but didn't attach a number.

I ran similar numbers last quarter. For 100 hours of synthetic speech via a commercial API like Resemble, you're looking at roughly $2500 to $4000, depending on voice tier and emotional modulation. That's $25-$40 per audio hour, just for generation. Now add compute and storage for your noise-augmentation pipeline.

Compare that to spending $500 on a quiet USB mic and paying five people $20 each to record for two hours. You get 10 hours of acoustically varied, real human speech that already contains natural breath noise, mic artifacts, and subtle prosodic inconsistencies. That's your baseline. Perturb that with speed, pitch, and simulated noise, and you've got a 50-hour dataset for under $1000, with full ownership and no synthetic bias.

The math only flips if you need thousands of speakers or linguistically rare phrases your cohort can't produce. For command phrases, the economics strongly favor small-scale, high-quality real data as the seed.

Your price comparison is the most concrete I've seen, thanks. What about the colocation angle? You're assuming you have a quiet space and five reliable people for the real recordings. If you're a remote team or don't have a consistent physical setup, that $500 mic cost might balloon into renting a studio or dealing with highly variable home recordings, which adds its own noise floor. Doesn't that narrow the cost gap?

Your numbers on cost vs. results are the exact reason I keep bouncing off these services. That 8% WER drop on a clean set looks great on a slide, but the minute you test it on a noisy warehouse floor or a bad car Bluetooth connection, the synthetic training just evaporates.

The real trap is thinking you can fix it by "layering in background noise post-generation." Now you're paying a premium for pristine audio and then spending more compute to deliberately make it worse. The economic model is backwards.

I'd be curious what your "other synthesis or perturbation methods" were. Did you benchmark against just taking a small real dataset and aggressively perturbing speed and pitch? For command phrases, that often gets you further for pennies.

That point about coherent acoustic models is critical. Even when you layer noise onto synthetic speech, the underlying "clean" signal still lacks the subtle artifacts of real microphone capsules and pre-amp noise, which means the model never learns the correlation between the speech signal and its own acoustic distortion. It's an uncanny valley for training data.

Your wake-word example is a perfect case where perturbation of real data works better. For a short command, the primary goal is often accurate detection across diverse noise conditions, not perfect phonetic richness. Synthetic data optimizes for the wrong metric there.

I'd add one caveat to the Upwork suggestion: managing remote contractors for consistent audio quality introduces its own overhead. You need very clear specs for mic type, gain levels, and room tone. Without that, you trade synthetic consistency for a different kind of variability that's harder to quantify. But you're right, the data ownership is a huge plus.