I've been conducting a rigorous evaluation of WellSaid Labs for potential use in generating system status announcements and automated incident voiceovers, where clarity and lack of auditory fatigue for on-call engineers is paramount. During my extended testing of the female voice 'Aria', I've identified a persistent and quite distracting issue with sibilance, particularly on consonant clusters like 's', 'sh', and 'ch'. This manifests as an exaggerated, high-frequency "hiss" that becomes pronounced in sentences containing technical terminology.
For example, rendering a standard deployment notification script:
```plaintext
Service deployment for 'search-scheduler' to cluster 'us-east-1-shared' is now complete. Initial health checks show successful shard allocation.
```
The words "search-scheduler", "us-east-1-shared", "checks", "show", and "shard" all trigger excessive sibilance that is harsh over headphones during a long on-call shift. I've attempted to mitigate this through the available API parameters and the web UI controls with limited success.
My testing methodology so far has included:
* Adjusting the `speech_rate` (both slower and faster) – slower exacerbates the issue, faster masks it slightly but reduces comprehensibility.
* Experimenting with the `pitch` variance – minor reductions can dampen the peak frequency but do not eliminate the core artifact.
* Inserting phonetic spelling or subtle text rewrites (e.g., "search-scheduler" to "search scheduler") – this is a workaround, not a fix, and breaks our standardization.
Given that a core SRE principle is to eliminate known sources of alert fatigue and cognitive irritation, this is a significant barrier to adoption. Has anyone else in the community performing similar technical evaluations encountered this with Aria or other WellSaid voices? More importantly, has anyone discovered a reliable configuration fix, an undocumented API parameter, or a specific text preprocessing technique that successfully mitigates this without compromising the natural flow of the speech?
I am particularly interested if WellSaid Labs support has provided any substantive engineering details on the cause—whether it's a training data artifact, a vocoder issue, or something addressable in post-processing. A comparison of the raw audio waveforms with a less sibilant voice like 'Mason' shows a clear spike in amplitude on those frequencies.
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