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Vol. 10, No. 12 [December 2024]


Paper Title :: Enhancing Answering Machine Detection through Adaptive Speech Transcription: A Telecommunications Industry Solution
Author Name :: Borovikov Evgeny
Country :: Dubai, UAE
Page Number :: 01-04
This study examines an innovative approach to Answering Machine Detection (AMD) leveraging the PocketSphinx speech recognition system, a lightweight and efficient tool based on Hidden Markov Models (HMM). The research conducts a comparative analysis between traditional cadence-based methods and a speech recognition system, trained on a specialized dataset of answering machine responses. The developed solution achieves high detection accuracy (90-98%) compared to traditional methods (70-80%) while maintaining optimal server resource utilization through its lightweight architecture. Key technical features include preconfigured analysis parameters, integration with Asterisk PBX through EAGI script, and support for both cloud and on-premise deployment. This research contributes to the field by demonstrating how efficient HMM implementation with specialized training data can provide high-performance AMD solutions while minimizing computational overhead.
Keywords: answering machine detection, hidden markov models, speech recognition, telecommunications, voice analysis, early media detection, asterisk pbx, lightweight architecture, call center automation, telco solutions.
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