Voice extraction by on-line signal separation and recovery

The paper presents a formulation and an implementation of a system for voice output extraction (VOX) in real-time and near-real-time realistic real-world applications. A key component includes voice-signal separation and recovery from a mixture in practical environments. The signal separation and extraction component includes several algorithmic modules with a variety of sophistication levels, which include dynamic processing neural networks in tandem with (dynamic) adaptive methods. These adaptive methods make use of optimization theory subject to the dynamic network constraints to enable practical algorithms. The underlying technology platforms used in the compiled VOX software can significantly facilitate the embedding of speech recognition into many environments. Two demonstrations are described: one is PC-based and is near-real-time, the second is digital signal processing based and is real time. Sample results are described to quantify the performance of the overall systems.