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Answering Machine Detection with Scikit-learn

Answering Machine Detection with Scikit-learn

Comprehensive software solution for call office powered by AI
October 22, 2022
5 minutes read
  • A call center faced the challenge of manually making thousands of calls daily, with a significant amount of time lost to encountering automated answering systems. Seeking a solution to this pain point, the client approached us with the request to bring automation into their business, aiming to streamline their calling process and efficiently manage the high volume of calls.
  • The task involved the automatic detection of answering machines based on the voice of the respondent, as well as the further automation of the call process. To tackle the challenge of identifying answering machines, our team utilized algorithms from scikit-learn, including LightGBM, CatBoost, and XGBoost. After rigorous testing and validation, we selected the algorithm that demonstrated the best performance, achieving 98% precision and 97% recall. Additionally, for sound processing and analysis, we employed the Librosa library, ensuring accurate and efficient handling of audio data.
Digital Ocean
1 Week
Solution Architecture Design
2 Weeks
Data Collection & Preprocessing
2 Weeks
Answering Machine Detection Model Development
8 Weeks
Web Application Development & AI Models Integration
1 Week
Deployment & Testing
  • One of the key challenges we faced was the absence of advanced technologies like Whisper and other popular speech-to-text tools at that time. This limitation meant we couldn't transform the task of detecting answering machines into an NLP classification problem. Consequently, our team had to approach this challenge by detecting answering machines at the level of sound waves, specifically focusing on mel-spectral coefficients. This approach required a more intricate understanding and analysis of audio data, as we had to rely solely on the characteristics of sound waves rather than textual content for accurate detection.
  • The second major challenge was ensuring effective multiprocessing of calls. Given the scenario with multiple operators handling a high volume of calls, frequent interruptions, and the frequent triggering of the answering machine detection model, managing a high load efficiently was crucial. We needed to design a system capable of concurrently processing numerous calls without degradation in performance or accuracy. This required the implementation of a robust multiprocessing architecture that could handle multiple call streams simultaneously, maintain the stability of the answering machine detection model under heavy load, and ensure minimal latency and resource contention. The technical intricacies involved optimizing thread management, load balancing, and real-time data handling to achieve seamless multiprocessing in a high-demand call center environment.
  • The first step our team took was to gather thousands of the client's call recordings, categorizing them into two groups: calls answered by humans and those responded to by answering machines. Based on this dataset, we embarked on developing a detection model. We compared neural network approaches with traditional machine learning techniques. After thorough analysis, we found that while neural networks offered high accuracy, traditional machine learning algorithms matched them in performance and offered faster processing times. This speed was crucial for the client's needs. Ultimately, we chose the classical approach, achieving an impressive 98% precision and 97% recall in our validation tests, thereby ensuring rapid and accurate detection of answering machines, which was essential for optimizing the client’s call process.
  • The second part of the solution involved integrating the developed model into the client's existing system, which was built on Asterisk and Django. This integration was crucial as it enabled the call center employees to automatically dial hundreds of thousands of numbers while effectively filtering out calls answered by answering machines. By implementing our model into this platform, we significantly enhanced the efficiency of the call process. The automated system reduced manual effort and time wastage, allowing the call center staff to focus on actual human interactions, thereby boosting overall business productivity. The seamless integration with Asterisk and Django ensured that the model worked harmoniously within the existing infrastructure, providing a robust and efficient solution to the client's challenges.
AI MVP Development
Call Centers
AI Driven Software Solutions
AI Chat-bot Development
AI MVP Development
Dedicated AI&ML Team
ERP/CRM Integrations
Kyiv, Ukraine
London, United Kingdom
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