What Is Text and Speech Analytics?

What Is Text and Speech Analytics?

Text and speech analytics is the practice of turning unstructured conversational data — chat transcripts, support tickets, recorded calls — into structured signal a business can act on. It covers transcription, intent classification, sentiment analysis, topic extraction, named-entity recognition, and compliance keyword spotting. In a 2026 voice-AI stack, the pipeline begins at ASR, layers on classifiers and judge-model graders, and emits per-turn metrics. FutureAGI’s overlap is the evaluation layer: are the transcripts accurate, is the sentiment tagging stable, is the escalation rate trustworthy.

Why It Matters in Production LLM and Agent Systems

Bad analytics output silently corrupts every downstream business metric. If ASR mistranscribes “cancel” as “complete” 3% of the time, the cancel-rate dashboard understates churn. If sentiment classification drifts after a model upgrade, “negative-sentiment surge” alerts fire on noise. If intent extraction misses a regulated keyword, compliance review misses a flag. None of these surface as system errors — they surface as wrong decisions made calmly.

CX leaders feel this when analytics dashboards stop matching reality. Compliance leads feel it during quarterly audits when call samples disagree with the analytics summary. ML engineers feel it when “we have analytics” turns out to mean a six-month-old classifier whose drift no one is monitoring. SREs feel it when the analytics pipeline becomes the bottleneck during a traffic spike — and analytics latency lags real-time intervention windows.

For 2026 voice-agent stacks the stakes are higher. Voice agents act on the analytics output: a sentiment classifier flags frustration, the agent escalates, the customer hears a different voice. If the classifier is wrong, the customer hears the wrong escalation. Unlike batch CCaaS analytics that informs a Monday morning review, voice-agent analytics feeds a real-time control loop where errors compound within a single call. FutureAGI’s role is to evaluate every stage of that loop.

How FutureAGI Handles Text and Speech Analytics

FutureAGI does not ship a speech-analytics product — that lives in the ASR provider, the conversational-AI platform, or the analytics tool of choice. FutureAGI’s role is to evaluate whether the analytics signal is trustworthy when it feeds an agent’s behavior or a quality dashboard. The relevant fi.evals surfaces are ASRAccuracy for the transcript boundary, Tone and IsPolite for sentiment proxies, ConversationResolution for intent capture, and CustomerAgentConversationQuality for end-to-end quality.

A real workflow: a healthcare-voice team runs nightly LiveKitEngine simulations across 12 caller personas and 8 scenarios. Each transcript is scored with ASRAccuracy (transcript correctness), Tone (politeness drift), CustomerAgentInterruptionHandling (barge-in correctness), and ClinicallyInappropriateTone (compliance). In production, traceAI-livekit emits spans for each ASR segment and LLM turn; the same evaluators run on a 5% production sample, and eval-fail-rate-by-cohort flags drift by language, codec, or call length.

Concretely, if ASRAccuracy falls 4 points on Spanish callers but Tone stays flat, the team knows the analytics layer is mistranscribing — not that the agent is being rude. Unlike a generic CCaaS analytics dashboard that reports aggregate scores, FutureAGI separates each stage of the analytics pipeline so engineers can fix the actual broken layer.

How to Measure or Detect It

Score each analytics stage independently — averaging across stages hides the failing layer:

• ASRAccuracy: transcript correctness against a reference; the floor for every downstream signal.
• Tone: classifies tone categories (polite, neutral, frustrated); track drift across time and language.
• ConversationResolution: scores whether the call reached a logical resolution — the highest-level intent metric.
• Word Error Rate by cohort: split WER by language, codec, network, and speaker; aggregate WER is misleading.
• Eval-fail-rate-by-cohort (dashboard signal): percentage of calls where any analytics evaluator fails, sliced by user cohort.

Minimal Python:

from fi.evals import ASRAccuracy, Tone

asr = ASRAccuracy()
tone = Tone()

asr_result = asr.evaluate(prediction=asr_transcript, reference=human_transcript)
tone_result = tone.evaluate(input=user_turn, output=agent_turn)
print(asr_result.score, tone_result.score)

Common Mistakes

• Treating ASR accuracy as the only quality signal. A transcript can be 95% accurate and still mislabel “cancel” — the 5% errors are usually the highest-impact words.
• Aggregating sentiment across the whole call. Per-turn sentiment is the actionable signal; call-average sentiment hides barge-in frustration spikes.
• Letting the analytics classifier drift unmonitored. Sentiment and intent classifiers retrain on shifting language; rerun evaluator scores on a held-out cohort weekly.
• Mixing transcript and analytics metrics in one number. Score them separately so a regression points to the actual broken layer.
• Scoring only success stories. Sample failed and escalated calls disproportionately; that is where analytics quality matters most.