What Is Speech Analytics?

What Is Speech Analytics?

Speech analytics is the automated extraction of structured insights from voice conversations — sentiment, topic, intent, agent compliance, escalation risk, and resolution outcome. Traditional contact-center speech analytics ran keyword spotting and statistical scoring over ASR transcripts; modern AI speech analytics uses LLM-based scoring on transcripts and audio. For voice AI agents, it functions as evaluation: did the agent follow the script, catch escalation cues, and resolve the customer’s issue? The answer is a layered analytics stack on top of ASR and audio capture.

Why It Matters in Production LLM and Agent Systems

Voice is opaque without analytics. A text agent’s failure leaves a logged transcript anyone can search; a voice agent’s failure leaves an audio file no one will listen to. Without speech analytics, voice AI runs blind: you have CSAT scores after the fact, complaints from the loudest customers, and no view into the long tail of degraded calls.

The pain shows up across roles. A voice-AI engineer ships a new prompt and discovers two weeks later that resolution rate dropped 6% — speech analytics would have caught it on day one. A QA lead gets asked to “review last week’s calls” and has 40,000 of them with no triage. A compliance team is audited on disclosure-statement adherence and has to review hundreds of calls manually because their analytics setup is keyword-based and misses paraphrased disclosures. A product manager wants to know whether the bot escalates correctly on emotional cues and has no per-call escalation-handling score.

In 2026 voice-AI stacks, speech analytics has converged with voice-agent evaluation. The same evaluators that score an agent in offline simulation are reused on live production calls; the same LiveKitEngine capture path that runs in test runs in production. The era of separate “speech analytics” and “voice agent evaluation” tooling is ending; both are operations on the same transcript-plus-audio object.

How FutureAGI Handles Speech Analytics

FutureAGI’s voice stack treats speech analytics as a continuum from offline simulation to live production. At the simulation level, simulate-sdk’s LiveKitEngine runs voice scenarios end-to-end with transcript and audio capture; results land in a TestReport with per-turn scores. At the evaluator level, voice-specific evaluators score the captured data: ASRAccuracy (transcription quality), AudioQualityEvaluator (audio fidelity), CaptionHallucination (transcription artifacts), ConversationResolution (did the call end with the customer’s need met), ConversationCoherence (logical flow), CustomerAgentContextRetention (did the agent remember earlier turns), CustomerAgentInterruptionHandling, CustomerAgentTerminationHandling. At the production level, traceAI captures live calls as spans, samples a percentage into the same evaluator pipeline, and emits a daily voice-agent-quality dashboard.

Concretely: an outbound sales-agent team runs a 1,000-call nightly simulation through LiveKitEngine covering languages, accents, objection types, and disclosure scenarios. They score each call with ConversationResolution, CustomerAgentObjectionHandling, ASRAccuracy, and a CustomEvaluation for disclosure-statement coverage. A model upgrade drops CustomerAgentObjectionHandling from 0.84 to 0.71; the team identifies that the new model is more concise and skips a follow-up question. They tune the prompt, re-run, and the score recovers. In production, they sample 5% of live calls into the same eval and watch for drift weekly. FutureAGI makes speech analytics into a regression-eval discipline.

How to Measure or Detect It

Speech-analytics signals to wire into voice-AI reliability:

• ASRAccuracy — transcription quality on captured audio; the foundation of every other analytic.
• ConversationResolution — did the call end with the user’s request handled.
• ConversationCoherence — logical and topical consistency across the conversation.
• CustomerAgentContextRetention — multi-turn memory quality.
• CaptionHallucination — transcription artifacts that corrupt downstream analytics.
• Per-language / per-accent score breakdown — fairness signal across user populations.
• Disclosure-coverage rate — compliance metric for regulated voice flows.

Minimal Python — score a captured voice interaction:

from fi.evals import ASRAccuracy, ConversationResolution

asr = ASRAccuracy()
resolution = ConversationResolution()

audio_score = asr.evaluate(audio_path="call.wav", reference_transcript=ref).score
res_score = resolution.evaluate(conversation=transcript).score
print(audio_score, res_score)

Common Mistakes

• Keyword-only analytics. Misses paraphrased disclosures, sentiment carried by tone, and any nuance the model cannot keyword-match.
• Ignoring audio quality as a confound. Bad audio causes bad ASR causes bad analytics. Score audio quality first.
• Single-language coverage. Voice agents run in real linguistic distributions. Specificity drops sharply on under-tested dialects.
• No per-call replay path. Aggregate scores are useful; individual call replay is what fixes problems. Persist transcripts and audio together.
• Evaluating only resolved calls. Abandoned and escalated calls carry the worst signal. Sample them disproportionately into eval.