Incorporating Knowledge Sources into Statistical Speech Recognition [Paperback]

Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.

The authors address the problem of developing efficient automatic speech recognition systems that maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training manageable and improving speech recognition performance.

Incorporating Knowledge Sources into Statistical Speech Recognition addresses the problem of developing efficient automatic speech recognition (ASR) systems, which maintain a balance between utilizing a wide knowledge of speech variability, while keeping the training / recognition effort feasible and improving speech recognition performance. The book provides an efficient general framework to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. It can be applied to many existing ASR problems with their respective model-based likelihood functions in flexible ways.

Incorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition efls3