Paper Info
Title
Reduced Complexity and Scaling for Asynchronous HMMS in a Bimodal Input Fusion Application
Abstract
The Asynchronous Hidden Markov Model (AHMM) can model the joint likelihood of two observation sequences, even if the streams are not synchronised. Previously this model has been applied to audio- visual recognition tasks. The main drawback of the concept is its rather high training and decoding complexity. In this work we show how the complexity can be reduced significantly with advanced run- ning indices for the calculations. Yet, the AHMM characteristics and its advantages are preserved. The improvement also allows a scal- ing procedure to keep numerical values in a reasonable range. In an experimental section we compare the complexity of the original and the improved concept and validate the theoretical results. Then the model is tested on a bimodal speech and gesture user input fusion task: Compared to a late fusion HMM an improvement of more than 10% absolute recognition performance has been achieved.
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1661386
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference
Keywords
Field
DocType
audio-visual systems,computational complexity,decoding,gesture recognition,hidden Markov models,image coding,speech coding,speech recognition,asynchronous HMM,asynchronous hidden Markov model,audio-visual recognition tasks,bimodal input fusion application,bimodal speech,decoding complexity,gesture user input fusion task
Asynchronous communication,Speech coding,Pattern recognition,Computer science,Gesture,Gesture recognition,Speech recognition,Artificial intelligence,Decoding methods,Hidden Markov model,Scaling,Computational complexity theory
Conference
Volume
ISSN
ISBN
5
1520-6149
1-4244-0469-X
Citations 
PageRank 
References 
2
0.41
6
Authors
2
Name
Order
Citations
PageRank
Marc Al-Hames11168.75
Gerhard Rigoll22788268.87