Abstract:
Recently a lattice structure for adaptive linear prediction using the least mean square gradient approach was proposed. This paper investigates the application of this me...Show MoreMetadata
Abstract:
Recently a lattice structure for adaptive linear prediction using the least mean square gradient approach was proposed. This paper investigates the application of this method to speech signals, and presents a number of implementations of the Adaptive Lattice Linear Prediction (ALLP) algorithm. These implementations allow flexible tradeoffs to be made between the computational requirements and the covergence properties of the algorithm. A measure of fast spectral transitions, inherent in certain implementations, is also described with particular application to detecting plosives in speech. Experimental results are discussed.
Date of Conference: 10-12 April 1978
Date Added to IEEE Xplore: 29 January 2003
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Linear Prediction ,
- Computational Requirements ,
- Speech Signal ,
- Least Mean Square ,
- Mean Square Error ,
- Steady State ,
- Convergence Rate ,
- Input Signal ,
- Time Resolution ,
- Faster Convergence ,
- Reflection Coefficient ,
- Spectral Changes ,
- Spectral Measurements ,
- Fast Changes ,
- Properties Of Algorithm ,
- Speech Data ,
- Forward Prediction ,
- Adaptive Step Size
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Linear Prediction ,
- Computational Requirements ,
- Speech Signal ,
- Least Mean Square ,
- Mean Square Error ,
- Steady State ,
- Convergence Rate ,
- Input Signal ,
- Time Resolution ,
- Faster Convergence ,
- Reflection Coefficient ,
- Spectral Changes ,
- Spectral Measurements ,
- Fast Changes ,
- Properties Of Algorithm ,
- Speech Data ,
- Forward Prediction ,
- Adaptive Step Size
