Evolution of Speech-to-Text (STT) Capability for Call Center Analytics
This diagram illustrates the progression of our Speech-to-Text initiatives, specifically focused on extracting valuable information from customer call recordings.
Overall AI Use Case: Speech To Text (Call Center Analysis)
Objective: To automatically transcribe and analyze spoken interactions from our customer call centers.
Business Value: Identify customer pain points, improve agent performance, ensure regulatory compliance (e.g., call disclosures), detect potential fraud, extract product feedback, and understand customer sentiment regarding financial products or services.
Path 1: Project v1 (Initial Proof-of-Concept / Basic Implementation)
AI Project: project-v1 (Basic Call Transcription)
Description: This was our initial foray into STT. The primary goal was to achieve basic transcription of call audio and attempt to extract a limited set of common entities.
Scope: Likely focused on shorter call segments or specific call types for manageability.
AI Model: model-text (Generic STT Model)
Description: This likely refers to an off-the-shelf or generally pre-trained STT model (e.g., from a cloud provider, or an open-source model like Whisper in its base form).
Characteristics: Good for general English transcription but may lack accuracy with financial jargon, acronyms, product names, or understanding nuanced customer intents specific to our institution. Not fine-tuned on our specific call center data.
Input Field: input_en_doc (Audio Snippet - MP3)
Description: The model ingested English audio, likely in a common, possibly compressed format like MP3. "Audio Snippet" suggests it might have processed shorter segments rather than entire, lengthy call recordings, perhaps due to initial processing limitations or a focused use case (e.g., analyzing only the call opening or closing).
Output Field: adress (Detected Address - Basic)
Description: From the transcribed text, this iteration attempted to identify and extract customer addresses.
Limitations: Given the generic model, the accuracy for addresses might have been moderate, potentially struggling with variations in how addresses are spoken, background noise, or non-standard formats. It might only capture parts of an address.
Path 2: Project v2 (Enhanced & Domain-Specific Implementation)
AI Project: project-v2 (Enhanced Entity Extraction & Accuracy)
Description: This represents a more mature and targeted STT solution, building upon the learnings and limitations of project-v1. The focus shifted to higher accuracy transcription and more sophisticated, domain-relevant Named Entity Recognition (NER).
Scope: Likely designed to handle full call recordings and extract a wider range of entities crucial for financial analysis.
AI Model: model-text (Fine-tuned STT & NER Model for Finance)
Description: This is a significantly improved model.
Fine-tuned STT: The base STT model was likely fine-tuned on a substantial corpus of our actual call center recordings. This improves its accuracy on our specific acoustic environments, common financial terminology, accents of our customer base, and product names.
Custom NER: A Named Entity Recognition component, possibly custom-trained or heavily adapted, was integrated to identify specific entities beyond just basic ones.
Characteristics: Higher transcription accuracy, especially for financial terms. Capable of identifying a broader and more relevant set of entities with greater precision.
Input Field: input_en_doc (Call Audio - WAV / High Fidelity)
Description: The model now likely processes full-length call audio. The "WAV / High Fidelity" suggests an input format that retains more audio detail (less compression), which generally leads to better STT performance.
Output Fields (from project-v2's model):
adress (Extracted Full Address - Verified):
Description: The system now extracts addresses with higher confidence and completeness. "Verified" might imply some post-processing or confidence scoring, possibly even cross-referencing with customer databases if integrated.
organization (Mentioned Financial Institutions/Products):
Description: A key enhancement. The model is now capable of identifying mentions of organizations (e.g., "XYZ Bank," "ABC Insurance," competitor names) and potentially specific financial products or services discussed during the call (e.g., "mortgage application," "platinum credit card"). This output is highly valuable for competitive analysis, product feedback, and compliance monitoring.
Summary for the AI Team:
The diagram shows our journey from a basic STT implementation (project-v1) using a generic model with limited output, to a more sophisticated, domain-adapted solution (project-v2).
Project-v2 leverages a fine-tuned STT model combined with targeted NER capabilities. This allows us to process higher fidelity, full-length call audio and extract richer, more accurate, and financially relevant information like complete addresses and mentions of specific organizations or products. This evolution significantly increases the business value derived from our call center audio data.
Legend Recap:
AI Use Case (Dark Blue): The overarching goal.
AI Project (Red): Specific initiatives to achieve the use case.
AI Model (Light Blue): The machine learning model performing the core task.
Output Field (Green): The data/information extracted by the model.
Input Field (Yellow): The data fed into the model.