Speech Analytics for Call Center Optimization
Project Overview:
Objective
The “Speech Analytics for Call Center Optimization” project aims to create a dataset for training machine learning models to analyze and extract valuable insights from recorded customer service calls using speech recognition. This dataset will enable call centers to enhance their operations, improve customer service quality, and streamline processes.
Scope
This project involves collecting audio recordings of customer service calls from various call centers, using Speech Recognition to transcribe them into text, and annotating them with relevant information, such as sentiment, topic, and call outcome.
Sources
- Call Centers: Collaborate with call centers and customer service providers to obtain recorded customer service calls.
- Transcription Services: Use automatic speech recognition (ASR) technology to transcribe audio recordings into text.
Data Collection Metrics
- Total Call Center Call Recordings: 20,000 recordings
- Transcribed Calls: 15,000
Annotation Process
Stages
- Sentiment Analysis: Annotate each call transcript with sentiment labels, such as “Positive,” “Negative,” or “Neutral,” to assess customer sentiment during the call.
- Topic Classification: Label each call transcript with topics or categories related to customer inquiries or issues, such as billing, technical support, or product inquiries.
- Call Outcome: Annotate the call outcome, indicating whether the issue was resolved, escalated, or required follow-up.
- Metadata Logging: Log metadata, including call date and time, customer ID (anonymized), agent ID (anonymized), and call duration.
Annotation Metrics
- Call Transcripts with Sentiment Labels: 15,000
- Topic Classifications: 15,000
- Call Outcomes: 15,000
- Metadata Logging: 15,000
Quality Assurance
Stages
Annotation Verification: Implement a validation process involving call center experts to review and verify the accuracy of sentiment labels, topic classifications, and call outcomes.
Data Quality Control: Ensure the removal of low-quality or irrelevant call transcripts from the dataset.
Data Security: Protect sensitive customer information and adhere to data privacy regulations.
QA Metrics
- Annotation Validation Cases: 1,500 (10% of total)
- Data Cleansing: Remove low-quality or irrelevant call transcripts
Conclusion
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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