Speech analytics is the automated transcription and analysis of voice calls, examining the words spoken, sentiment, silence and talk patterns to surface insight at scale. It converts recorded or live phone conversations into text, then analyzes that text to detect what happened on the call, how the customer felt and how the agent performed. Contact centers use it to review every call rather than the small sample a supervisor could listen to by hand.
In short
- Speech analytics transcribes voice calls, then analyzes the transcript for words, sentiment, silence and talk-over.
- It lets contact centers review 100% of calls instead of the handful a supervisor can listen to manually.
- It surfaces recurring topics, compliance risks and coaching moments automatically from spoken language.
- It is voice-focused, whereas conversation intelligence spans digital channels such as chat and email as well.
- The most useful implementations tie each insight to the exact moment in the call that produced it.
How speech analytics works
Speech analytics starts by converting audio into text using automatic transcription. It can run after a call, on recorded audio, or in real time as the conversation happens. Once the call is text, the software analyzes it for the language used, the customer’s sentiment, periods of silence, agents and customers talking over each other, and whether specific phrases or compliance statements were said. Because the process is automated, a contact center can analyze every call instead of the small percentage a quality team could realistically listen to.
What speech analytics measures
Beyond the words themselves, speech analytics reads the shape of a call to infer how it went.
| Signal | What it captures |
|---|---|
| Words and phrases | Topics raised, keywords and required compliance statements |
| Sentiment | Whether the customer’s tone was positive, neutral or negative |
| Silence | Dead air and hold time that can signal friction |
| Talk-over | Agent and customer speaking at once, a sign of tension |
| Outcome | Whether the issue appears resolved by the end of the call |
Speech analytics in the contact center
In a contact center, speech analytics is used to raise call quality, catch compliance issues and find the reasons customers keep calling. Because it reviews complete call volume rather than a sample, supervisors coach on evidence instead of anecdote, and leaders spot emerging problems earlier. Speech analytics is the voice-focused member of a wider family: conversation intelligence applies the same idea across digital channels too, so chat, email and messaging are read alongside calls. Kaizo is a neutral-by-design QA and coaching platform, native to Zendesk and Salesforce, whose focus is evolving toward conversation intelligence for support teams.
Frequently asked questions
Is speech analytics the same as conversation intelligence?
No. Speech analytics is voice-focused, analyzing phone calls. Conversation intelligence is broader, covering digital channels such as chat, email and messaging as well as voice, so it reads the whole conversation base rather than calls alone.
Can speech analytics work in real time?
Yes. It can run after a call on recorded audio or live as the conversation happens, which allows real-time prompts and alerts as well as after-the-fact review.
What does the silence signal tell you?
Long silences or dead air can indicate an agent searching for information, a system delay, or customer frustration. Tracking silence helps teams find friction that the words alone would not reveal.
Does speech analytics require recording every call?
It analyzes the audio a contact center already captures. For full coverage, calls need to be recorded or streamed, after which each one is transcribed and analyzed automatically rather than sampled by hand.
Related terms
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