Speech analytics is software that analyzes recorded or live customer calls to find out what was said, how it was said and why customers called. It converts speech into searchable data (by phonetic indexing or full speech-to-text transcription), then spots keywords, categorizes calls and flags compliance or quality issues, mainly in contact centers.
Key Takeaways
- Speech analytics mines recorded or live calls for topics, keywords, emotion and silence, and is used mainly by customer contact centers.
- The two main engine types are phonetic search (fast, phoneme-based) and large-vocabulary continuous speech recognition (LVCSR, full transcription).
- Transcription accuracy is usually measured with word error rate (WER); search accuracy is measured with precision and recall.
- Call-recording consent laws, data-protection law such as the GDPR and the PCI DSS card-data standard all apply to call analytics programs.
- Under the EU AI Act, using AI to infer the emotions of people in the workplace has been prohibited since 2 February 2025, with medical and safety exceptions.
There’s a digital solution to almost every problem nowadays. One of those solutions that have proven to be imperative to any business that wants to focus on excellent customer service is through speech analytics transcription.
In this article, we take a look at exactly what speech analytics is, what it does, how to use it and the benefits of investing in a speech analytics tool.
What Is Speech Analytics Software?
Speech analytics is software that analyzes recorded or live customer calls; it uses speech recognition to turn audio into searchable data and is used mainly in call centers and contact centers. Despite emails, social media and live chat bots being increasingly popular with the public, phone calls remain a core customer service channel for many businesses.
With the use of a speech recognition tool, such as speech analytics, it can quickly produce reports on customer interactions and provide supervisors and other senior staff with extensive data in a comprehensive format that can be turned into actionable insights to improve the experience for both customers and agents.
How Does Speech Analytics Work?
Every provider of speech analytics will have slightly different features, however, there are some simple basics that every package should come with.
The first basic component is speech-to-text transcription. This software will transcribe conversations between agents and customers and create readable documents, many systems also label who is speaking (a process called speaker diarization), in some systems even when one party speaks over another.
The second basic component is keyword and phrase spotting. Predetermined words and phrases can be programmed into speech analytics software and when these are spoken by any party, it can be grouped, flagged and reported to managers.
This could be anything from ensuring compliance methods to finding out what customers are currently needing or demanding.
How To Use Speech Analytics
How to use speech analytics all depends on the results you are expecting from it. If quality assurance and compliance are top of the list, then the main focus should be on fine-tuning and accuracy of the software before implementing it.
Once this is mastered, the software should easily flag any call agents who may not be following the correct procedures and managers can take steps to increase training.
In the initial stages of this, there may be errors within the results, but by taking the time to accurately declare these as false positives and putting the time in to analyse everything, the results should become more reliable over time, although no speech analytics system is error-free.
If you are wanting speech analytics to help improve the business processes, such as understanding why customers are calling and any recurring issues or queries, this is more an ad-hoc task.
Using an in-house analyst, you can programme speech analytics to query language within the software and tailor it to the exact nature of the business and results you are looking for.
Any good speech analytics software vendor will help you get set up with your new systems and can show you exactly how to programme and use these tools to get the exact results you want, so a team that is new to these tools can ask for onboarding and training as part of the contract.
The Benefits of Speech Analytics
There are many benefits of speech analytics software and these all vary between sectors and their own personal needs, however, there are some common themes within the reasons to make the investment.
Firstly, managers can identify agents who are struggling, this could be staff providing the wrong information to not being able to handle irate customers appropriately.
This allows managers to take the right steps before quality is badly impacted and even reduce staff churn. The more confident an agent is, the more likely they may be to stay in the role.
Secondly, customer insights can be extensively reported on. This could be a wide range of requirements such as products or services not currently on offer but have high demand, to issues with current services and products that are frequently being experienced.
Action can be swiftly taken to provide customers with what they want and need, help to increase revenue and reduce customer attrition.
Lastly, businesses can identify process, product and service issues much more easily and even spot problems that could lead customers to defect to a competitor.
Do You Need Speech Analytics?
This is a simple element to analyse. If you want to know why your customers are calling your agents, are your agents performing to the best of their ability and do you need to know the impact of these calls on customer satisfaction and relationship?
If so, speech analytics is worth evaluating, provided call volumes justify the cost and the business can meet call-recording consent and data-protection rules.
Any good contact centre should be seeking every avenue to improve their customer satisfaction levels and analytics software that recognises speech patterns is one practical way to do so.
Phonetic vs. Transcription-Based Speech Analytics
Speech analytics engines mainly use one of two approaches. Some vendors build their own engine, while others license a third-party speech recognition engine.
| Approach | Basic unit | Strengths | Limits |
|---|---|---|---|
| Phonetic indexing | Phonemes (most languages have only a few tens of them) | Fastest to process because the recognition grammar is very small | Output is a stream of phonemes, not readable text, so word error rate cannot be measured |
| LVCSR (large-vocabulary continuous speech recognition, also called speech-to-text or ASR) | Words and word sequences (bi-grams, tri-grams) | Produces full transcripts, faster queries, higher accuracy, and can surface new business issues nobody searched for | Needs a vocabulary of hundreds of thousands of words to match against |
Transcription-based (LVCSR) systems also make it possible to run text analytics, such as sentiment analysis, on the transcript. Sentiment analysis, also called opinion mining, uses natural language processing to identify and measure attitudes and emotions in language.
Real-Time vs. Post-Call Speech Analytics
Speech analytics can run in two modes. Real-time speech analytics spots keywords or phrases on live audio and raises alerts while the call is still in progress, for example to prompt an agent with a required disclosure. Post-call speech analytics processes recorded calls afterward, a technique also known as audio mining, and is used for trend reports, categorization and quality reviews.
Core Features of Speech Analytics Software
- Transcription: converts calls to text so they can be searched and read.
- Speaker separation (diarization): answers the question “who spoke when” by splitting the audio into speaker turns, so agent and customer speech can be analyzed separately.
- Keyword and phrase spotting: finds predefined words, such as a competitor name, “cancel” or a mandatory compliance script.
- Call categorization: groups calls by reason, for example billing, complaints or unsatisfied customers.
- Trend reporting: shows the words and phrases used most often in a period and whether their use is rising or falling.
- Acoustic analysis: measures the emotional character of the speech and the amount and location of speech versus silence in a call.
How Is Speech Analytics Accuracy Measured?
Speech analytics accuracy is measured in two ways, because transcription quality and search quality are different things.
Word error rate (WER)
Word error rate is the standard measure of transcription accuracy. It is calculated as WER = (S + D + I) / N, where S is the number of substituted words, D the number of deleted words, I the number of inserted words and N the number of words in the correct reference transcript. A WER of 0 means a perfect transcript. For example, if a 10-word reference sentence comes back with one wrong word and one missing word, the WER is 2 / 10, or 20%. Because insertions are counted, WER can exceed 100%.
Precision and recall
When speech analytics is used to search calls, precision is the share of returned results that are relevant, and recall is the share of all relevant calls that the search found. The two pull against each other: as accuracy goes up, the detection rate usually goes down. Because individual recognition errors affect search results unevenly, a low WER does not by itself guarantee good search results, so buyers should test both measures on their own recorded calls.
How to Implement Speech Analytics: Step by Step
- Define the goal. Choose one or two outcomes first, such as compliance monitoring, reasons for calls, or agent coaching.
- Check the legal basis. Confirm recording consent, privacy notices and data-retention rules for every region you serve (see the next section).
- Test engines on your own audio. Ask vendors to process a sample of your real calls, including accents and noisy lines, and compare WER, precision and recall.
- Build categories and phrase lists. Start with a small set of call reasons and compliance phrases, then expand.
- Tune for false positives. Review flagged calls, mark false positives and refine the phrase lists.
- Connect results to action. Feed findings into agent training programs, quality scorecards and product teams.
- Review regularly. Track the same metrics over time so improvements can be measured.
Speech analytics usually sits alongside other contact center systems; for the wider operational picture, see this guide to contact center management and this overview of phone features that call centers use.
Legal and Compliance Rules for Speech Analytics
Speech analytics depends on recorded calls, so recording and data-protection law applies. The points below are general information as of September 2026, not legal advice.
- US call-recording consent: laws vary by state. Some states require only one party to consent, while others, including California, Florida, Maryland and Pennsylvania, generally require all parties to consent. In 2006 the California Supreme Court ruled (Kearney v. Salomon Smith Barney) that a caller in a one-party state recording someone in California is subject to California’s all-party consent rule.
- EU data protection (GDPR): the GDPR has applied since 25 May 2018. It covers personal data, which the European Commission defines as information relating to an identified or identifiable individual, so recordings and transcripts of identifiable callers are in scope. Fines can reach €20 million or 4% of annual worldwide turnover, whichever is greater.
- Payment card data (PCI DSS): the Payment Card Industry Data Security Standard regulates how businesses store, process and transmit cardholder data and sensitive authentication data, which includes card verification codes and PINs. Contact centers that take card payments by phone need to keep this data out of recordings and transcripts, or protect it as the standard requires.
- EU AI Act emotion rules: Article 5 of the EU AI Act prohibits AI systems that infer the emotions of a natural person in the workplace or in education, except for medical or safety reasons. The prohibition has applied since 2 February 2025, so emotion-detection features aimed at employees, such as call agents, need careful legal review in the EU.
Speech Analytics vs. Speech Recognition vs. Call Recording
| Technology | What it does | Main output |
|---|---|---|
| Call recording | Captures and stores the audio of calls | Audio files |
| Speech recognition (ASR) | Converts spoken words into text | Transcripts |
| Speech analytics | Searches, categorizes and measures calls using recognition plus topic, emotion and silence analysis | Reports, alerts, trends and scores |
Put simply, call recording captures the conversation, speech recognition makes it readable, and speech analytics turns many conversations into business insight. For more on the transcription layer, see AI transcription features and benefits, and for where the insights are usually stored, see what CRM software is.
Frequently Asked Questions
What is speech analytics used for?
Speech analytics is used to find out why customers call, check that agents follow compliance scripts, identify agents who need coaching, spot product or process problems, and track trends in what customers say over time.
Is speech analytics the same as speech recognition?
No. Speech recognition converts speech into text, while speech analytics uses speech recognition as one step and then analyzes topics, keywords, emotion and silence across many calls to produce reports and alerts.
What is the difference between phonetic and LVCSR speech analytics?
Phonetic speech analytics indexes calls as streams of phonemes and is the fastest to process. LVCSR speech analytics produces full word transcripts, which allows faster queries, higher accuracy and discovery of issues nobody searched for.
How is speech analytics accuracy measured?
Transcription accuracy is measured with word error rate, calculated as substitutions plus deletions plus insertions divided by the number of words in the reference transcript. Search accuracy is measured with precision and recall.
Is it legal to analyze recorded customer calls?
It can be, but the rules depend on location. US states differ on whether one party or all parties must consent to recording, the GDPR applies to recordings of identifiable people in the EU, and PCI DSS governs card data captured on calls. Businesses should get legal advice for their own situation.
