Gistly
Subscribe to newsletter
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
Every day, your contact center generates thousands of conversations. Each one contains signals—compliance risks, coaching opportunities, customer frustration, competitive mentions, upsell cues. The problem? Most teams only hear a fraction of them.
Speech analytics changes that. It applies AI to every recorded call, converting audio into structured, searchable data that QA managers, trainers, and operations leaders can actually act on.
This guide covers what speech analytics is, how it works under the hood, and how to evaluate platforms—so you can move from sampling 2-3% of calls to analyzing 100%.
Speech analytics is the process of using AI to extract meaningful information from voice conversations. It combines automatic speech recognition (ASR), natural language processing (NLP), and machine learning to transcribe calls, identify patterns, and surface insights at scale.
In a contact center context, speech analytics replaces manual call monitoring. Instead of QA teams listening to a handful of calls per agent per week, the technology processes every conversation—flagging compliance violations, scoring agent performance, and detecting customer sentiment automatically.
Speech analytics falls under the broader category of conversation intelligence—platforms that analyze voice, chat, and email interactions to improve business outcomes. While conversation intelligence covers all communication channels, speech analytics focuses specifically on the voice layer.
Modern speech analytics platforms follow a five-stage pipeline:
The process starts with call recordings. Most platforms integrate with telephony systems (cloud PBX, CCaaS platforms, or SIP-based infrastructure) to ingest audio automatically. Some support real-time streaming for live analysis; others process recordings post-call.
Audio is converted to text using ASR engines. Accuracy matters here—especially for contact centers handling calls in multiple languages or with heavy accents. The best platforms achieve 90%+ accuracy across languages and support speaker diarization—identifying who said what in a conversation. For teams operating in India or Southeast Asia, multilingual transcription that handles code-switching (mixing Hindi and English mid-sentence, for example) is critical.
Once transcribed, NLP models analyze the text for:
This is where speech analytics moves beyond transcription into actual intelligence. The platform applies rules, scorecards, and ML models to evaluate conversations:
Insights are delivered through dashboards, alerts, and integrations. QA managers see aggregate trends. Team leads get agent-level scorecards. Compliance officers receive real-time flags. The data feeds into coaching workflows, training programs, and operational decisions.
These terms overlap, and vendors use them inconsistently. Here is how they differ:
| Term | Scope | Focus |
|---|---|---|
| Speech analytics | Voice calls only | Extracting insights from what was said (words, phrases, topics) |
| Voice analytics | Voice calls only | Analyzing how it was said (tone, pitch, pace, emotion) |
| Conversation intelligence | Voice + chat + email + video | Full-spectrum analysis of all customer interactions, with coaching and workflow integrations |
In practice, most modern platforms combine all three. A speech analytics engine that does not analyze tone is incomplete. A conversation intelligence platform that ignores voice is missing the richest data source. When evaluating vendors, look for platforms that cover both the what and the how across channels.
The most fundamental shift speech analytics enables is moving from sample-based QA to 100% call auditing. When you are only reviewing 2-3% of calls manually, you are making decisions based on incomplete data. Speech analytics removes the sampling gap entirely.
Regulated industries—financial services, healthcare, collections, insurance—require specific disclosures on every call. Speech analytics flags calls where required language was missing or where agents made unauthorized commitments, before those gaps become regulatory findings.
Instead of QA managers spending hours finding coachable moments, speech analytics surfaces them automatically. Low-scoring calls, missed closing techniques, and customer escalation patterns are identified and queued for review—turning coaching from reactive to systematic.
Aggregate speech analytics reveals shifts that no individual call reviewer would catch: a spike in cancellation intent, increased mentions of a competitor new feature, or a recurring complaint about a recent product change. These signals inform product, marketing, and CX strategy.
When every call is transcribed and scored automatically, QA teams spend less time on manual listening and more time on high-value activities: coaching, process improvement, and compliance program design. Contact centers that deploy speech analytics typically reduce QA review time by 60-80%.
Speech analytics automates the QA workflow end-to-end. Calls are scored against weighted criteria—opening compliance, empathy markers, issue resolution, proper closing—without a human listener. QA managers shift from reviewing individual recordings to reviewing exceptions, trends, and aggregate performance patterns.
For teams building or refining their scoring framework, combining speech analytics with a structured QA scorecard methodology ensures consistency across evaluators and eliminates the subjectivity that plagues manual QA programs.
For collections teams, insurance call centers, and financial services operations, speech analytics provides an auditable compliance layer. Every call is checked against disclosure requirements, regulatory scripts, and prohibited language. Violations trigger alerts in real time or during post-call review.
The compliance use case is particularly strong in India, where the Digital Personal Data Protection (DPDP) Act requires organizations to demonstrate that customer data is handled according to consent and purpose-limitation principles. Speech analytics creates the audit trail that proves compliance—or catches violations before they become enforcement actions.
Sales teams use speech analytics to understand what separates closed deals from lost ones. Win/loss patterns, objection handling effectiveness, competitive positioning mentions, and pricing discussion dynamics are all quantifiable when every call is analyzed.
The insight compounds over time. After analyzing thousands of sales conversations, patterns emerge that no individual manager would spot: which talk-to-listen ratio correlates with higher close rates, which objection responses actually work, and which competitor claims are gaining traction in the market.
Sentiment trends, repeat contact patterns, and escalation rates—tracked across thousands of calls—give CX teams early warning signals. When negative sentiment spikes for a specific product line or customer segment, the team can intervene before churn accelerates.
Support teams benefit most when speech analytics is tied to ticket-level data. Correlating call sentiment with resolution outcomes, NPS scores, and repeat contact rates builds a predictive model for customer health that goes beyond survey-based measurement.
New agent ramp time drops when trainers can pull real call examples—both excellent and poor—from a searchable library. Speech analytics platforms make it possible to build training curricula from actual conversations rather than hypothetical scenarios.
The best implementations create a feedback loop: speech analytics identifies skill gaps across the team, training addresses those gaps with targeted call examples, and post-training analytics measure whether performance actually improved. This data-driven approach to agent development replaces the guesswork of traditional training programs.
Not all platforms are equal. Here is what to assess:
Ask for accuracy benchmarks on your specific call types—not just English in ideal conditions. If your contact center handles calls in Hindi, Tamil, Bahasa, or other regional languages, test with real recordings. Code-switching accuracy (mixing languages within a single call) is a meaningful differentiator.
Can you build custom QA scorecards that match your actual evaluation criteria? Can you define your own compliance rules, keyword libraries, and alert thresholds? Rigid, one-size-fits-all scoring will frustrate QA teams within weeks.
Some use cases demand real-time analysis (live agent guidance, compliance alerts during calls). Others are well-served by post-call batch processing (QA scoring, trend reporting). Understand which matters for your operation and confirm the platform supports it.
Speech analytics data is most valuable when it connects to your CRM, ticketing system, workforce management tools, and coaching platforms. Evaluate API depth, native integrations, and data export capabilities.
Enterprise speech analytics deployments historically took months. Modern cloud-native platforms can deliver initial insights within days. Ask vendors for their typical time-to-value—and verify with reference customers.
Call recordings contain sensitive customer data. Evaluate the platform approach to PII masking, data residency, encryption, access controls, and regulatory certifications (SOC 2, GDPR, DPDP Act for India-based operations).
India BPO industry handles millions of customer conversations daily across dozens of languages. Speech analytics adoption in this market faces unique challenges—and opportunities—that global platforms often overlook.
Most speech analytics platforms were built for English-first markets. Indian contact centers operate in a fundamentally different linguistic environment. A single call might start in English, switch to Hindi for rapport-building, include Marathi technical terms, and close in English. Standard ASR engines struggle with this code-switching pattern because they are trained on monolingual datasets.
Platforms built for the Indian market handle multilingual transcription natively—not as an add-on or beta feature, but as a core capability tested against real Indian call recordings with real accent and code-switching patterns.
The Digital Personal Data Protection Act (2023) creates specific obligations for organizations processing personal data through voice channels. Speech analytics platforms operating in India need:
Many Indian BPOs still rely on manual QA sampling—reviewing 2-5% of calls per agent per month. At a 300-agent contact center handling 500 calls per agent per month, that is 150,000 monthly conversations with only 3,000-7,500 reviewed. Speech analytics closes this gap entirely, moving to 100% automated auditing where every conversation is scored, flagged, and searchable.
Speech analytics is converging with three broader trends:
Generative AI for summarization — Instead of keyword-based reports, next-generation platforms use LLMs to generate natural-language call summaries, coaching recommendations, and trend narratives.
Real-time agent assistance — Moving beyond post-call analysis to in-call guidance: prompting agents with relevant knowledge base articles, compliance reminders, and suggested responses as the conversation unfolds.
Multi-modal analysis — Combining voice analytics with screen recording, chat transcripts, and email threads to build a complete picture of every customer interaction, not just the voice component.
What is the difference between speech analytics and call recording?
Call recording captures audio. Speech analytics analyzes it—transcribing the conversation, identifying patterns, scoring quality, and surfacing insights. Recording is the raw material; analytics is the intelligence layer.
How accurate is AI-powered speech analytics?
Modern ASR engines achieve 85-95% accuracy depending on audio quality, accent, and language. For business-critical use cases like compliance monitoring, look for platforms that allow human review of flagged items rather than relying solely on automated scoring.
Can speech analytics work with calls in multiple languages?
Yes, but capability varies widely. Some platforms support 5-10 major languages. Others handle 100+ languages with varying accuracy. For contact centers in India, verify support for Hindi, Tamil, Telugu, Marathi, and code-switching between English and regional languages.
How long does it take to deploy speech analytics?
Cloud-native platforms can process initial recordings within days. Full deployment—including custom scorecards, integrations, and team training—typically takes 2-6 weeks. Gistly delivers a findings report within 48 hours of receiving call data.
What is the ROI of speech analytics?
ROI varies by use case. QA teams typically see 60-80% reduction in manual review time. Compliance teams reduce audit findings and regulatory penalties. Sales teams report higher win rates from data-driven coaching. The fastest ROI comes from compliance monitoring—a single prevented violation often justifies the platform cost.
Does speech analytics work with existing telephony systems?
Most cloud-native speech analytics platforms integrate with major CCaaS providers (Genesys, Five9, NICE, Talkdesk), cloud PBX systems, and SIP-based infrastructure. Integration depth varies—some platforms offer native connectors, others rely on API-based recording ingestion. Verify compatibility with your specific telephony stack before committing.
Gistly audits every conversation automatically — compliance flags, QA scores, and coaching insights in 48 hours.