The reliability of Speech Analytics depends on three factors: the quality of the input recordings, the performance of the speech recognition and analysis algorithms, and the system configuration.
Recent solutions have made significant progress, but it is essential to understand their limitations in order to use them effectively.
Recent solutions have made significant progress, but it is essential to understand their limitations in order to use them effectively.
The 3 pillars of reliability
🎯 Accuracy rate
The best Speech-to-Text exceed 85–90% accuracy in recognizing words in everyday speech. Strong accents, fast speech, or specific jargon remain areas of concern.
🎙️ Audio Quality
A poor recording (mono, background noise, distortion) automatically compromises the results. Clean stereo audio is a non-negotiable prerequisite for a reliable analysis.
⚖️ Bias & Interpretation
Models may reflect biases in their training data. Customization of the grids and human oversight remain essential.
How Cross CX Reliability
- State-of-the-art generative AI connectors combined with a proprietary engine — see the AI connectors.
- Business configuration for your real-world scenarios using customizable grids.
- Management reports that track transcription quality over time — see the quality reports.
The modules in question
