Case Studies

l 5min

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Arabic Voice AI

Author

Khalid Ghiboub

Table of Content

1 .

The Challenge

2 .

Evaluation: Testing Munsit STT Against the Existing System

3 .

Implementation: Replacing the ASR Layer

Powering the Future with AI

Join our newsletter for insights on cutting-edge technology built in the UAE

Key Takeaways

Munsit improved Arabic IVR intent recognition, especially for Gulf dialect speech and natural language queries.

‍

The telco reduced intent fallback rates, resulting in fewer misrouted calls and better self-service success.

‍

Integration required replacing only the ASR layer, with no changes to existing routing logic or IVR workflows.

Full call transcripts provided new visibility into customer needs, emerging issues, and service demand trends

A GCC telecom operator reduced IVR intent fallback rates and misrouted calls by replacing generic Arabic speech recognition with Munsit's Gulf dialect speech-to-text. The deployment improved call routing accuracy, lowered agent workload, enhanced customer satisfaction, and unlocked valuable transcript-based operational insights.

‍

The Challenge

IVR systems are the first point of contact in any telco's customer operation. When they work, they route callers quickly and reduce pressure on human agents. When they fail to understand what a customer is saying, callers repeat themselves, get sent to the wrong queue, or end up with a live agent anyway, creating the exact cost and friction the IVR was meant to eliminate.
‍

A GCC telecom operator had built its Arabic IVR on a third-party ASR platform that worked reasonably well for the fixed phrases it was trained on. But as the company's product range grew and customer questions became more varied, recognition quality started slipping. Callers were speaking in natural language, using Gulf dialect, mentioning specific product names, and mixing Arabic and English in the same sentence, none of which the underlying model handled reliably.
‍

The operations team tracked what they called an "intent fallback rate": the share of Arabic calls where the IVR couldn't determine what the caller wanted and sent them to a general agent queue. Over two years, that number had been climbing. By the time a replacement evaluation started, roughly one in four Arabic calls was bypassing the IVR entirely. Each of those calls required a live agent to start from scratch, adding to handle time and hurting first-call resolution rates

‍

This is some text inside of a div block.

Evaluation: Testing Munsit STT Against the Existing System

The telco's technical team ran a six-week parallel evaluation. A portion of incoming Arabic calls was routed through Munsit STT while the existing Arabic ASR handled the rest. Both streams fed into the same intent classification layer, and outcomes were measured against human-labeled transcripts from the same call sample.
‍

Munsit STT delivered higher intent detection accuracy across the board. The biggest improvement came on Arabic dialect natural language inputs, the exact pattern that had been causing the most fallback failures. The evaluation also tested calls where customers switched between Arabic and English mid-sentence, typically when naming a product or describing a technical issue. Munsit handled these code-switching calls more reliably, maintaining recognition on the Arabic portions even when English terms appeared inline.

‍

This is some text inside of a div block.

Heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Implementation: Replacing the ASR Layer

Following the evaluation, the telco began a phased rollout, starting with its busiest service line: prepaid plan management. This line had the highest call volume and the worst fallback rate, making it the highest-impact place to start.

‍

The Munsit Speech-to-text API was integrated directly into the IVR platform, replacing the recognition layer while leaving everything else, intent classification, routing logic, and agent queues untouched. The integration ran through the IVR vendor's API layer, with CNTXT AI's technical team supporting the process over three weeks. The main items requiring calibration were the audio encoding format and the streaming configuration. Once those were resolved, the integration was stable.

‍

What Changed

Fewer misrouted calls, better customer experience, and a new data source from a single infrastructure change.
‍

After deployment, the intent fallback rate on the prepaid line dropped noticeably. Calls that had been landing in the general queue were now being correctly identified and sent to the right self-service path or specialist queue. Average handle time for agent-assisted calls from this line also fell because agents were receiving better-qualified calls rather than unrouted general inquiries.
‍

Post-call satisfaction scores for the IVR experience improved on the affected service line. In survey responses, the most common theme was simply that the system understood callers, something that had been a recurring complaint before.
‍

There was also a benefit the operations team hadn't fully anticipated:
‍

Structured transcripts as a data asset. The previous system produced only binary routing decisions with no text record. Munsit produced full transcripts, giving the operations team a new way to analyze what customers were actually asking, including emerging inquiry types, product confusion patterns, and seasonal volume shifts.

The telco is currently running the same evaluation on two additional service lines, with a roadmap for full IVR migration over the following two quarters.

‍

See how Munsit performs on real Arabic speech

Evaluate dialect coverage, noise handling, and in-region deployment on data that reflects your customers.

Explore

Result

Arabic IVR performance has a ceiling set by the quality of the ASR underneath it. No amount of tuning the routing logic or intent classification helps if the system can't accurately transcribe what callers say in Gulf dialect or across code-switched speech.
‍

Replacing the ASR layer with Munsit STT is a targeted infrastructure change; it doesn't require rebuilding the IVR. The results are measurable from deployment and extend further as the change rolls out across more service lines.

‍

FAQ

Powering the Future with AI

Join our newsletter for insights on cutting-edge technology built in the UAE

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Arabic Voice AI

Case Studies

From Audio Archive to Published Article: Arabic Podcast Transcription for Digital Media

Arabic podcast transcription: See how a MENA media company used Munsit STT to transcribe 200 episodes, cut article production time by 55%, and boost organic search traffic.

Arabic Voice AI

Case Studies

Arabic Voiceover at Scale: How a MENA Broadcaster Integrated TTS Into Its Production Workflow

See how a MENA broadcaster used Faseeh Arabic TTS to go from 7-day voiceover turnarounds to same-day production without compromising on audio quality.

Enterprise AI

Case Studies

How a GCC Telco Built an Arabic Speech-to-Text Dataset from Call Archives

A GCC telco used Munsit STT and specialized Arabic annotation to turn 10,000 call recordings into a labeled Arabic speech-to-text dataset, improving intent-classification on Gulf dialects in six weeks

Arabic Voice AI

Case Studies

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

A GCC telecom operator reduced IVR intent fallback rates and misrouted calls by replacing generic ASR with Munsit's Gulf dialect Arabic speech-to-text. See how

Arabic Voice AI

Case Studies

Arabic TTS in Islamic Finance: How a Mobile Banking App Reduced Support Calls with Munsit

Learn how a regional Islamic finance institution used Munsit's Arabic text-to-speech (Faseeh) in its mobile banking app to reduce support calls and improve product comprehension.

Arabic Voice AI

Case Studies

Arabic Call Center QA at Scale: How a UAE Bank Moved from Sampling to Full Coverage

A UAE retail bank replaced manual Arabic call center QA with Munsit STT, achieving 100% call coverage, Gulf dialect accuracy, and compliance-ready transcripts at scale.

Arabic Voice AI

Case Studies

Arabic TTS for Government Digital Services: How Natural Voice Closed an Accessibility Gap

See how Arabic TTS improved accessibility in GCC government digital services with clearer voice guidance, better form completion, and fewer support issues.

Enterprise AI

Case Studies

How a Gulf Government Authority Cut Call Center Escalations with Arabic Speech Recognition

A Gulf government authority cut call center escalations and reduced compliance response time from days to hours using Munsit's Gulf dialect Arabic STT. See how purpose-built Arabic speech recognition outperformed generic ASR models.

Speech Recognition

Tech Deep Dive

Arabic ASR: A Guide to Why Dialects Are Key to Accuracy

A deep dive into how Automatic Speech Recognition (ASR) works for Arabic. Learn why dialects break generic models and why a dialect-first approach is essential for enterprise accuracy.

Compliance

How-To

From Transcription to Intelligence: Building Compliant Arabic Voice AI for Regulated Industries

Learn how to build compliant Arabic voice AI for GCC banking and healthcare. Navigate PDPL, UAE data laws, dialect complexity, and audit-ready voice intelligence

Machine Learning

Tech Deep Dive

Arabic Acoustic Modeling: A Guide to Vowels, Emphatics, and Dialects

A deep dive into the challenges of Arabic acoustic modeling for ASR. Learn about short vowels, diacritics, emphatic consonants, and dialectal shifts.

Performance

Tech Deep Dive

WER vs. CER: How to Measure Arabic ASR Accuracy

A guide to Word Error Rate (WER) and Character Error Rate (CER) for Arabic speech recognition. Learn why WER fails for Arabic and how to evaluate ASR accuracy.

Enterprise AI

Case Studies

The Strategic Value of Arabic Speech to Text for Enterprises

Learn about the strategic value of Arabic speech-to-text for enterprises. A deep dive into the market opportunity, business impact, and technical reality of Arabic ASR.

Machine Learning

How-To

The Foundation of Voice: How to Build High-Quality Arabic Speech Training Data

Learn how to build high-quality Arabic speech datasets for ASR and TTS. A deep dive into data curation, quality control, and handling dialectal diversity.

Ai Architecture

How-To

Streaming vs. Batch Transcription: A Guide to Real-Time Transcription Architecture

Learn when to use streaming vs. batch transcription for your enterprise. A deep dive into real-time transcription architecture, trade-offs, and hybrid approaches.

Arabic Voice AI

Product

Introducing Munsit: The First Arabic Speech-to-Text App Built for You

Introducing Munsit, the first Arabic transcription app built for dialects, code-switching, and real-world use. Download now for fast, accurate Arabic voice-to-text.

Performance

How-To

How to Optimize Real-Time Arabic ASR Performance

A deep dive into optimizing real-time Arabic ASR. Learn about latency, throughput, model compression (quantization, pruning), and streaming architectures.

Voice Technology

Tech Deep Dive

How Natural Arabic Text-to-Speech Works: A Guide to Prosody, Waveforms, and Voice Quality

A deep dive into how natural Arabic Text-to-Speech (TTS) is made. Learn about prosody, neural vocoders like HiFi-GAN, and the challenges of dialects and diacritization.

Speech Recognition

Tech Deep Dive

How Arabic Dialect Recognition Works

A deep dive into how Arabic Dialect Identification (ADI) works. Learn about the phonetic and morphological clues AI uses to distinguish Arabic dialects.

Voice Technology

How-To

A Guide to Designing Arabic Voice UX

Learn how to design effective Arabic voice UX. A deep dive into handling Arabic-English code-switching, designing for accessibility, and navigating cultural context.

Arabic Voice AI

Product

Beyond Multilingual Models: Why Arabic Voice AI Needs Its Own Technology

Explore the linguistic, dialectal, and cultural reasons why generic multilingual models fail for Arabic and why a ground-up approach to voice AI is essential for the Arab world.

Natural Language Processing

How-To

Arabic NLP: A Guide to Dialects, Code-Switching, and ROI

A comprehensive guide to enterprise Arabic NLP. Learn why global models fail on dialects and code-switching and how to achieve ROI with a regionally grounded approach.

Performance

Tech Deep Dive

Arabic Dialects and Domain Context: Why Generic Models Fail Business Accuracy Tests

Discover why generic ASR models fail on Arabic dialects and domain-specific terms. See how dialect-aware Arabic ASR achieves up to 6.5x better accuracy for business.

Ai Architecture

How-To

A Guide to Sovereign AI Architecture, GPU Infrastructure, and Hybrid Deployments

Learn about Sovereign AI architecture, from GPU infrastructure to hybrid cloud deployments. A deep dive into the strategic imperative for nations like the UAE and Saudi Arabia.

Ai Architecture

Product

A Guide to Retrieval-Augmented Generation (RAG) for Arabic Conversational AI

Learn how Retrieval-Augmented Generation (RAG) makes Arabic conversational AI more accurate. A deep dive into RAG architecture, challenges, and applications.

Compliance

How-To

Data Sovereignty in the UAE Public Sector

Learn how to navigate data sovereignty in the UAE public sector. A comprehensive guide to the PDPL, deployment models, and sovereign cloud solutions.

Arabic Voice AI

The Future of Arabic Speech Technology: 2025 Trends & Beyond

Explore the future of Arabic speech technology in 2025 and beyond, including AI voice agents, dialect support, speech recognition, and emerging trends.

Home

Blog

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Last update :

June 24, 2026

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Case Studies

Arabic Voice AI

Author

Sarra Turki

Khalid Ghiboub

5min read

Table of Content

1 .

The Challenge

2 .

Evaluation: Testing Munsit STT Against the Existing System

3 .

Implementation: Replacing the ASR Layer

Bring Arabic Voice AI to production

Native‑level Arabic STT & TTS

Built for GCC gov & enterprises

Sovereign and on‑prem deployment

Contact Sales

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Key Takeaways

Munsit improved Arabic IVR intent recognition, especially for Gulf dialect speech and natural language queries.

‍

The telco reduced intent fallback rates, resulting in fewer misrouted calls and better self-service success.

‍

Integration required replacing only the ASR layer, with no changes to existing routing logic or IVR workflows.

Full call transcripts provided new visibility into customer needs, emerging issues, and service demand trends

‍

The Challenge

‍

Lorem ipsum dolor

Evaluation: Testing Munsit STT Against the Existing System

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

‍

Training Data Deficiencies

The most significant contributor to AI hallucinations is the data on which the models are trained. LLMs learn from vast datasets scraped from the internet, which contain a mixture of factual information, opinions, misinformation, and biases. Several specific data-related issues can lead to hallucinations:

Enterprise Use Cases for Arabic Voice AI in 2025

The move to dialect-aware Arabic ASR is unlocking a new wave of enterprise applications across the GCC and MENA regions. Organizations are moving beyond basic transcription to sophisticated Arabic speech analytics.

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Implementation: Replacing the ASR Layer

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

‍

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Building better AI systems takes the right approach

We help with custom solutions, data pipelines, and Arabic intelligence.

Learn more

What Changed

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Fewer misrouted calls, better customer experience, and a new data source from a single infrastructure change.
‍

There was also a benefit the operations team hadn't fully anticipated:
‍

Structured transcripts as a data asset. The previous system produced only binary routing decisions with no text record. Munsit produced full transcripts, giving the operations team a new way to analyze what customers were actually asking, including emerging inquiry types, product confusion patterns, and seasonal volume shifts.

The telco is currently running the same evaluation on two additional service lines, with a roadmap for full IVR migration over the following two quarters.

‍

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Result

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

‍

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

Understanding the origins of AI hallucinations is the first step toward mitigating them. The phenomenon is not a single problem but rather a complex issue with multiple contributing factors.

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Arabic speech technology is rapidly advancing in 2025, driven by massive multilingual models and new Arabic-centric foundation models.

FAQ

Bring Arabic Voice AI to production

Native‑level Arabic STT & TTS

Built for GCC gov & enterprises

Sovereign and on‑prem deployment

Contact Sales

Thank you! Your submission has been received!

Oops! Something went wrong while submitting the form.

Start free.
Pay when you are ready.

10,000 credits. Test Munsit with your own audio, in your own dialect, and see the accuracy for yourself.

Start Free

Talk to Sales

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Powering the Future with AI

Key Takeaways

The Challenge

Evaluation: Testing Munsit STT Against the Existing System

Heading

Implementation: Replacing the ASR Layer

What Changed

See how Munsit performs on real Arabic speech

Result

FAQ

Powering the Future with AI

Related articles

From Audio Archive to Published Article: Arabic Podcast Transcription for Digital Media

Arabic Voiceover at Scale: How a MENA Broadcaster Integrated TTS Into Its Production Workflow

How a GCC Telco Built an Arabic Speech-to-Text Dataset from Call Archives

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Arabic TTS in Islamic Finance: How a Mobile Banking App Reduced Support Calls with Munsit

Arabic Call Center QA at Scale: How a UAE Bank Moved from Sampling to Full Coverage

Arabic TTS for Government Digital Services: How Natural Voice Closed an Accessibility Gap

How a Gulf Government Authority Cut Call Center Escalations with Arabic Speech Recognition

Arabic ASR: A Guide to Why Dialects Are Key to Accuracy

From Transcription to Intelligence: Building Compliant Arabic Voice AI for Regulated Industries

Arabic Acoustic Modeling: A Guide to Vowels, Emphatics, and Dialects

WER vs. CER: How to Measure Arabic ASR Accuracy

The Strategic Value of Arabic Speech to Text for Enterprises

The Foundation of Voice: How to Build High-Quality Arabic Speech Training Data

Streaming vs. Batch Transcription: A Guide to Real-Time Transcription Architecture

Introducing Munsit: The First Arabic Speech-to-Text App Built for You

How to Optimize Real-Time Arabic ASR Performance

How Natural Arabic Text-to-Speech Works: A Guide to Prosody, Waveforms, and Voice Quality

How Arabic Dialect Recognition Works

A Guide to Designing Arabic Voice UX

Beyond Multilingual Models: Why Arabic Voice AI Needs Its Own Technology

Arabic NLP: A Guide to Dialects, Code-Switching, and ROI

Arabic Dialects and Domain Context: Why Generic Models Fail Business Accuracy Tests

A Guide to Sovereign AI Architecture, GPU Infrastructure, and Hybrid Deployments

A Guide to Retrieval-Augmented Generation (RAG) for Arabic Conversational AI

Data Sovereignty in the UAE Public Sector

The Future of Arabic Speech Technology: 2025 Trends & Beyond

How a GCC Telco Cut Misrouted Calls by Fixing Arabic IVR Speech Recognition

Bring Arabic Voice AI to production

Key Takeaways

The Challenge

Evaluation: Testing Munsit STT Against the Existing System

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Implementation: Replacing the ASR Layer

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Building better AI systems takes the right approach

What Changed

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Result

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Training Data Deficiencies

Training Data Deficiencies

Enterprise Use Cases for Arabic Voice AI in 2025

Bring Arabic Voice AI to production

Start free. Pay when you are ready.

Start free.
Pay when you are ready.