10 papers analyzed
These studies suggest that deep learning models are effective in various speech processing tasks, including formant frequency tracking, speech enhancement, and tempo estimation in musical audio signals.
Deep learning models have significantly advanced the field of speech processing, including tasks such as speech tempo estimation. This synthesis explores various research efforts that leverage deep learning techniques to estimate and track speech tempo, highlighting the key methodologies and findings from multiple studies.
Deep Learning for Beat Tracking and Tempo Estimation:
Formant Frequency Estimation and Tracking:
Robust Speech Duration Modeling:
Fundamental Frequency Estimation:
Speech Enhancement and Dereverberation:
Deep learning models have proven to be highly effective in various aspects of speech processing, including tempo estimation, formant frequency tracking, and speech enhancement. These models leverage advanced neural network architectures to achieve superior performance, robustness, and accuracy compared to traditional methods. The integration of deep learning techniques continues to push the boundaries of what is possible in speech processing, offering promising solutions for real-world applications.
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