For human contact-center teams
AI call quality monitoring and contact-center analytics
Connect speech estimates to human call review—with clear boundaries around decisions and outcomes.
A useful contact-center workflow separates recordings, transcripts, model estimates and reviewer judgments. This guide explains those roles, including voice analytics and PAIR agent–customer matching and its integration responsibilities.
Keep the layers distinct
What AI can add to call review
Recording a call, assigning a reviewer’s quality score and estimating expressed speech signals are different activities. A recording preserves material to review. A reviewer applies the organization’s criteria. A model supplies estimates that must be interpreted in context.
Speech estimates can be evaluated as inputs to a human-led review process. They do not, by themselves, establish customer satisfaction, agent quality, compliance or a reason for a caller’s behavior.
Transcript and expressed signals
Voice analytics for contact centers
A transcript describes what was said. Estimates of vocal expression describe aspects of how speech was delivered. Together with the recording and conversation context, these can be considered by a human reviewer; neither is a substitute for an agreed review rubric.
The documented API response includes tasks such as asr, diarization, emotion, strength, positivity, speaking_rate, hesitation and engagement. Returned labels and probabilities describe model estimates, not a person’s inner state. A continuous intensity score is a magnitude, not an emotion category.
From call material to review
An integration must handle suitable recordings, check which outputs are available, and present them with enough context for reviewers. Any selection of calls, quality-scoring model, coaching process or reporting layer must be designed and validated downstream; it is not implied by receiving these API fields.
Explore the speech emotion and behavior API for output and integration details, and speech emotion recognition for the recognition task itself.
A downstream application decision
Agent–customer matching and call routing
Agent–customer matching means assigning an available human agent according to an integration’s objectives, constraints and approved information. The routing decision belongs to the application; it is not an emotion-recognition output or a guaranteed “best” match.
Where speech estimates may fit
PAIR is in production for real-time agent–customer matching in call centers. Its routing decisions belong to the application layer, not the emotion-recognition API. Each integration still requires a defined purpose, evaluation and clear ownership of routing logic. Intent, purchase and payment outcomes are not returned API signals.
What must be confirmed
Discuss the interfaces, data requirements, evaluation design, consent, fairness and retention safeguards for your deployment with the team. Production availability does not guarantee a particular outcome or establish that a specific integration or demonstration is available.
Read about downstream intent inference and methodological limits. Discuss agent–customer matching requirements.
Design before automation
An integration-led workflow
Define what is being reviewed
Set a lawful purpose and recording permissions. Choose suitable audio and an explicit human-review question.
Confirm the returned outputs
Map documented outputs to the actual recordings and show uncertainty. Do not invent confidence or treat inferred attributes as verified facts.
Design review and decisions
Assign responsibility for reviewer criteria, escalation, retention and any downstream decision. Test the whole workflow before relying on it.
Security is a separate evaluation track: see deepfake detection for contact centers. An emotion estimate must not be repurposed as a fraud or honesty verdict.
Measure, don’t assume
How to evaluate a pilot
A bounded evaluation should start with a baseline, a defined recording sample and a human-reviewed error analysis. Separate how well signal estimates fit the task from whether a downstream workflow changes a business measure.
- First contact resolution (FCR): whether an issue is resolved within the organization’s defined first-contact scope.
- Average handling time (AHT): the organization’s defined average time spent handling interactions.
- Customer satisfaction (CSAT): a defined satisfaction measure, such as an explicitly collected survey response—not a synonym for a speech-emotion label.
Choose measurement definitions and comparison conditions in advance. These measures are evaluated downstream; this page makes no claim that speech analysis will improve them.
Responsible human-led review
Limits, consent and oversight
Speech expression is ambiguous. Noise, language, speaker and domain differences may affect interpretation. A model estimate does not reveal what an employee or customer feels, believes or intends.
Review workplace fairness, access, recording permissions and retention before introducing analysis. Human review should include appropriate context and a way to question consequential interpretations. Do not use an estimate on its own as a verdict about an employee.
See behavioral signal processing: methodology and limitations for the fuller methodological discussion.
Discuss your contact-center integration
Bring your intended review process, audio conditions, available evidence and decision responsibilities to the discussion. Technical evaluators can explore the speech emotion and behavior API; operations teams can discuss your contact-center integration.
Next step
Plan a bounded evaluation
Discuss the audio, workflow and evidence your team needs before making deployment decisions.