Voice of customer

Voice of customer analytics for recorded feedback

Analyze recorded support calls, interviews, and voicemails with transcripts, emotion, and speaking-style scores. Compare patterns across your recordings and find passages for your team to review.

For teams building this workflow into their own product. Start with prerecorded English audio; Oruk returns structured analysis through the API.

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Illustrative output

Recorded feedback, scored for emotion and tone

Honestly, I was ready to cancel — but the way your team handled it completely turned me around.

relieved 0.74sincere 0.63hopeful 0.41

Example labels for this interface concept. Analyze your recording to inspect actual model output.

Unified analysis request

curl https://speech-api.oruk.ai/v1/audio/analysis \
  -H "Authorization: Bearer $ORUK_API_KEY" \
  -H "X-Request-ID: $(uuidgen)" \
  -F "model=oruk-resonance" \
  -F "file=@recording.wav"

Example implementation

Where Oruk fits in your workflow

Your input: A batch of English voicemails, interviews, or support recordings linked to feedback records and existing product themes.
Oruk returns: Transcripts and timed acoustic labels that your application can join to those records.
Your application: Group reviewed passages by theme or cohort in your feedback system. Keep links to source audio so a product team can investigate a pattern.

Evaluate the result in your pilot

Assess whether reviewers find more relevant feedback within a fixed review budget. Check that differences across cohorts persist after accounting for audio quality and sampling.

How it works

Collect the recordings

Support calls, voicemails, user interviews, and post-call surveys — any prerecorded English audio you already keep.

Score emotion and style

Read a transcript with emotion and speaking-style labels from the audio, aligned to timed segments.

Compare and review

Compare vocal-expression labels across recordings and cohorts, then listen to the passages behind a pattern.

What you can build

Add vocal-expression scores to customer feedback
Compare expression patterns over time and by cohort
Find passages for follow-up review with your team
Attach acoustic tone to interview and survey transcripts

Deployment safeguards

Treat emotion labels as acoustic measurements, not statements of intent
Validate on the audio conditions, languages, and accents in your program
Keep multiple labels and uncertainty rather than one definitive state
Disclose recording and analysis according to applicable consent rules

Responsible-use guidance

Related resources

AI call analytics Contact center analytics Voice call sentiment analysis Sales call analysis Meeting speech understanding Qualitative research speech analysis Speech analytics API Speech emotion recognition API API capabilities and scope API documentation Build a speaker-turn review workflow Stream transcription and phrase emotion Pricing