Abstract
Customer care services in many organizations lack real-time and objective evaluation of agent performance, often relying on manual auditing of recorded calls and basic text-based analysis. This paper presents a Multimodal Tone Sensitivity Analyzer, an AI-driven platform that addresses these limitations through integrated analysis of speech and textual data. The system consists of key components including audio input processing, speech-to-text transcription, natural language sentiment analysis, and acoustic emotion detection. The frontend interface enables users to upload or record calls, while the backend processes data using advanced AI models for transcription and evaluation. Audio signals are analyzed to detect emotional tones such as happiness, anger, sadness, and neutrality, while textual transcripts are evaluated for communication quality and agent effectiveness. A multimodal evaluation module combines these outputs to generate performance insights and structured reports. The system also supports scalable data storage and efficient retrieval of analysis results. This approach provides an automated, accurate, and cost-effective solution for improving customer service quality monitoring in modern call center environments.
Keywords
Multimodal Analysis, Speech Recognition, Sentiment Analysis, Voice Emotion Detection, Natural Language Processing, Customer Care Analytics, Call Center Monitoring, Acoustic Analysis, AI-Based Evaluation, Performance Analysis, Real-Time Processing, Cloud-Based System, Data Analytics, Automated Auditing
Citations
IRE Journals:
J. Immanuel Kevin , Dr. K. Ponmozhi "Multimodal Tone Sensitivity Analyzer for Customer Care Services" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2123-2131 https://doi.org/10.64388/IREV9I9-1715448
IEEE:
J. Immanuel Kevin , Dr. K. Ponmozhi
"Multimodal Tone Sensitivity Analyzer for Customer Care Services" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715448
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