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Recording-quality diagnostics help your app distinguish a likely pronunciation issue from audio that may be difficult to analyze reliably. Successful speech-analysis responses can include a top-level recording_quality object. It reports signal measurements, a quality status, and a recommended way to handle the result.

Example

Quality status

The initial SNR bands are: Moderate or unavailable reverberation prevents a definitive good status. High reverberation, material clipping, or extremely quiet speech can produce a poor status. score_action describes how a future quality-aware integration can handle the pronunciation result: For schema version 1, recommendation.code is always one of the following values:

Metrics

SNR is returned only when the clip contains enough low-energy context and active speech to estimate both the noise floor and speech level. Otherwise, estimated_snr_db is null.

Reason codes

For schema version 1, every value in the reasons array comes from the following exhaustive set: