AI Case Studies: Call Centre Audio

Challenge

The primary challenge was managing the sheer volume of audio files generated by thousands of telephone operator interactions. Additionally, the distinct local accent and nuanced speech patterns of the Midlands posed a significant hurdle for accurate transcription. These factors necessitated a tailored approach to ensure the AI model could effectively interpret the audio data.

Solution

To address these challenges, we undertook the following steps:

• Accent Training: We trained the AI model specifically on the Midlands accent. This customization was crucial in achieving a transcription accuracy rate of over 90%, ensuring that the local dialect and speech patterns were accurately captured.
  
• Efficient Data Processing: Once the audio was transcribed into text, the results were imported into a database and searched with pattern matching. This enabled us to efficiently search and identify the five critical calls within a remarkably short timeframe of just a few days.
  
• Parallel Processing with Threading: What we found most interesting, is that even though the AI returned the result before the time taken for the audio to play out, we implemented a timer to stagger the phone calls to the endpoint. Using a technique known as threading, we could initiate the processing of several calls in parallel, resulting in an efficient solution.
  

Outcome

The implementation of the Azure AI solution resulted in a highly efficient and accurate transcription process. The tailored training on the Midlands accent significantly enhanced the model’s performance, allowing for precise identification of critical calls. The use of threading for parallel processing further streamlined the operation, demonstrating the potential for AI solutions to handle complex, large-scale audio data challenges effectively.

Training the model on the Midlands accent achieved more than 90% accuracy. Subsequently, the results from the AI were imported as text into a database, we were able to efficiently pinpoint and identify the five critical calls within the remarkably short timeframe of just a few days.

Client Satisfaction

This project not only met the client’s needs and budget but also highlighted the importance of customizing AI models to accommodate local linguistic variations, ultimately paving the way for future advancements in audio-to-text technology.

Manually listening to hundreds of hours of recorded audio is unfeasible due to the significant time and human resources required, making automated solutions essential for efficient analysis.