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Springer - Incorporating Knowledge Sources into Statistical ASR

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Description

Developing efficient automatic speech recognition (ASR) systems requires a careful balance. Engineers must manage wide speech variability while keeping training and recognition efforts feasible. This book from Springer provides a solution to these challenges by addressing how to improve speech recognition performance effectively. Readers will find an efficient general framework designed to incorporate additional knowledge sources into state-of-the-art statistical ASR systems. This approach allows for better performance without overwhelming the system's computational resources. The methodology is versatile and can be applied to many existing ASR problems through the use of model-based likelihood functions in flexible ways. As part of the Lecture Notes in Electrical Engineering series (Volume 42), this text serves as a technical resource for those working in AI, machine learning, and speech technology. It offers a structured way to integrate specialized knowledge into statistical models to enhance accuracy and system efficiency.

Key Features

Provides an efficient general framework to integrate additional knowledge sources into modern statistical ASR systems.

Addresses the challenge of balancing speech variability with manageable training and recognition efforts.

Offers practical applications for many existing ASR problems using model-based likelihood functions.

Part of the professional Lecture Notes in Electrical Engineering series (Volume 42) by Springer.

Focuses on improving speech recognition performance through advanced statistical methodologies.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
19 March 2009
Listed Since
07 August 2008

Barcode

No barcode data available

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