What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain.
Speech style accommodation refers to shifts in style that are used to achieve strategic goals within interactions. Models of stylistic shift that focus on speciﬁc features are limited in terms of the contexts to which they can be applied if the goal of the analysis is to model socially motivated speech style accommodation. In this paper, we present an unsupervised Dynamic Bayesian Model that allows us to model stylistic style accommodation in a way that is agnostic to which speciﬁc speech style features will shift in a way that resembles socially motivated stylistic variation.
State-Space Kalman Filters 7.2 Sample-Adaptive Filters
µ α w(m) α
7.3 Recursive Least Square (RLS) Adaptive Filters 7.4 The Steepest-Descent Method 7.5 The LMS Filter 7.6 Summary
daptive filters are used for non-stationary signals and environments, or in applications where a sample-by-sample adaptation of a process or a low processing delay is required.
EURASIP Journal on Applied Signal Processing 2003:10, 1016–1026 c 2003 Hindawi Publishing Corporation
Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications
Chandranath R. N. Athaudage
ARC Special Research Center for Ultra-Broadband Information Networks (CUBIN), Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia Email: firstname.lastname@example.org
Alan B. Bradley
Institution of Engineers Australia, North Melbourne, Victoria 3051, Australia Email: email@example.com.