Glossary term

What is speech analytics?

Speech analytics is the automatic analysis of conversations (calls and written exchanges) to extract metrics, topics, tone and other data from what was said and how the dialogue unfolded. It turns recordings into structured data you can build dashboards on and make decisions from.

Traditionally a manager listens to a 2–3% sample of calls and draws conclusions by feel. Speech analytics removes that limit: every conversation is transcribed, annotated and turned into a set of metrics — automatically and across 100% of the volume.

Dozens of characteristics are extracted from a single call: duration and share of silence, speech rate and filler words, sentiment and its trend, topics and keywords, action items, script adherence, personal-data detection. It covers both "what was said" (content) and "how it was said" (prosody, conversation dynamics).

On top of that data you get dashboards and trends, semantic search "by meaning, not by words," and configurable automations and alerts. Speech analytics is the core around which quality control, agent coaching and data preparation for AI are assembled.

The key difference from plain transcription: transcription gives you text, speech analytics gives you understanding. Here the text is just an intermediate layer on top of which metrics are computed and structured data is extracted.

How it works in the platform

In the platform, speech analytics is the core: each recording is scored with a set of system metrics (conversation dynamics, prosody, sentiment, compliance, content), plus your own AI metrics defined by your rules. Everything syncs to an analytical store that powers dashboards, semantic search and automations.

Related terms

Call transcription Call transcription is the automatic conversion of a conversation's audio recording into text with timing attached to each turn. It answers the question "what was said" and serves as the base layer on top of which any conversation analysis runs. Diarization Diarization is the automatic splitting of a conversation recording into turns by speaker: who spoke and when. It answers the question "who spoke," whereas speech recognition answers "what was said." Sentiment analysis Sentiment analysis is the automatic determination of the emotional coloring of a conversation: positive, negative or neutral. It shows how participants feel about the subject of the conversation and how their mood shifts over the course of the dialogue. Semantic search Semantic search is search by meaning rather than exact word match: it finds records close in meaning to the query even when they use different wording. It answers the question "what was the conversation about," whereas ordinary search looks for literal occurrences of phrases.

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