Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications. The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided. The reader will:
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Learn how automatic speech recognition can be used with robustness in real-world applications
Robust Automatic Speech Recognition establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques which have been developed over the past thirty years, with an emphasis on practical methods that have proven to be successful and which are likely to be further developed for future applications.
The strengths and weaknesses of robust enhancing speech recognition techniques are carefully analyzed, and a guide to selecting the best methods for practical applications is provided. The book covers noise-robust techniques designed for acoustic models, which are based on both Gaussian mixture models and deep neural networks.
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