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Speech Analytics Platform

PRJ-AZURE-AI-053

Call center analytics with sentiment analysis

~8 min read Intermediate
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Intermediate
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Implementation Guide

Comprehensive step-by-step deployment guide

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Estimated Monthly Cost

~$38/mo on minimal config
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Business ContextDifficulty in extracting actionable insights from vast volumes of unstructured c…

The Problem

  • Difficulty in extracting actionable insights from vast volumes of unstructured call center audio data.
  • Manual review processes for call quality and sentiment are time-consuming, inconsistent, and prone to human error.
  • Lack of real-time visibility into customer sentiment and emerging issues, hindering proactive customer service interventions.

The Solution

  • Utilizes Azure Speech Services to accurately transcribe call center audio into text in real-time.
  • Applies Azure Text Analytics, including Sentiment Analysis, to identify emotional tone and key topics within customer interactions.
  • Leverages Power BI for interactive dashboards and reports, providing comprehensive visualization of speech analytics data.

Business Value

  • Increases customer satisfaction scores (CSAT) by 15% through proactive issue resolution identified by sentiment analysis.
  • Reduces average call handling time (AHT) by 10% by quickly surfacing relevant customer context to agents.
  • Improves agent performance and training effectiveness by 20% through data-driven insights from call transcripts.
  • Achieves 99.9% accuracy in speech-to-text transcription for enhanced data reliability and analysis.

Risk Mitigation

  • Addresses data privacy concerns by implementing robust data anonymization and access controls for sensitive customer information.
  • Mitigates risks of biased sentiment analysis models through continuous monitoring, retraining, and diverse data sets.
  • Ensures scalability and high availability of the platform to handle fluctuating call volumes and data processing demands.
  • Reduces integration complexities by leveraging native Azure services and established APIs for seamless data flow.
GRC MappingNIST AI Risk Management Framework (AI RMF) v1.0: Govern, Map, Measure, Manage fu…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF) v1.0: Govern, Map, Measure, Manage functions for AI system lifecycle.
  • ISO/IEC 42001:2023 (AI Management System): Controls for responsible development and use of AI systems.
  • GDPR (General Data Protection Regulation): Principles for processing personal data, especially voice recordings and sentiment.
  • HIPAA (Health Insurance Portability and Accountability Act): Safeguards for protected health information (PHI) in call center interactions.

Security Controls Implemented

  • Access Control: Azure Active Directory (AAD) for role-based access to Speech Services and Text Analytics resources.
  • Data Encryption: Azure Storage encryption at rest and in transit for transcribed audio and analysis results.
  • Logging and Monitoring: Azure Monitor and Azure Log Analytics for tracking platform activity and security events.
  • Data Anonymization: Implementation of PII detection and redaction within Azure Text Analytics for sensitive data.
  • Network Security: Azure Virtual Networks and Network Security Groups (NSGs) to restrict access to platform components.

Audit Evidence

  • Azure Activity Logs and Diagnostic Logs for all Speech Services and Text Analytics operations.
  • Azure Policy compliance reports demonstrating adherence to data residency and security standards.
  • Data Protection Impact Assessments (DPIAs) for the processing of voice and sentiment data.
  • Model validation reports for sentiment analysis accuracy and bias detection.

Regulatory Alignment

  • GDPR Article 5 (Principles relating to processing of personal data) and Article 32 (Security of processing).
  • HIPAA Security Rule (45 CFR Part 164, Subpart C) for administrative, physical, and technical safeguards.
  • CCPA (California Consumer Privacy Act) Section 1798.100 (Consumer Rights) regarding personal information.
  • PCI DSS (Payment Card Industry Data Security Standard) Requirement 3 (Protect stored cardholder data) if payment info is processed.

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Architecture Diagram

PRJ-AZURE-AI-053 Architecture

Technology Stack

Speech Services
Text Analytics
Power BI
Sentiment Analysis

Complete Documentation

Prerequisites

Contributor or Owner role
Azure CLI 2.x configured
Terraform >= 1.5 (optional)
Active Azure subscription
Service Principal with RBAC
1

Clone & Authenticate

Clone the repository and authenticate with Azure CLI using your service principal or interactive login.

az login && az account set --subscription
2

Review RBAC Assignments

Review the required role assignments and ensure your identity has the correct permissions in the target resource group.

az role assignment list --assignee
3

Initialize Infrastructure

Run Terraform init and plan to preview the Azure resource changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all Azure resources in your target subscription.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the Azure Portal and check Azure Monitor for any alerts or issues.

az monitor activity-log list --resource-group

Deployment Guide

Step-by-step instructions to deploy this project

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Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

View on GitHub

Video Tutorial

Watch the complete walkthrough video

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