Name
Abstract | ||
|---|---|---|
In this paper, we provide theoretical results on the problem of optimal stream weight selection for the two stream classification problem. It is shown that in the presence of estimation or modeling errors using stream weights can decrease the total classification error. Specifically, we show that stream weights should be selected to be proportional to the feature stream reliability and informativeness. Next, we turn our attention to the problem of unsupervised stream weights computation in real tasks. Based on the theoretical results we propose to use models and "anti-models" (class-specific background models) to estimate stream weights. A nonlinear function of the ratio of the inter- to intra-class distance is proposed for stream weight estimation. The resulting unsupervised stream weight estimation algorithm is evaluated on both artificial data and on the problem of audiovisual speech classification. Finally, the proposed algorithm is extended to the problem of audiovisual speech recognition. It is shown that the proposed algorithms achieve results comparable to the supervised minimum-error training approach for classification tasks under most testing conditions. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TASL.2008.2011513 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
index terms— multi-stream weights estimation,feature stream reliability,unsupervised stream weights computation,audiovisual speech classification,unsupervised stream-weights computation,unsupervised stream weight estimation,optimal stream weight selection,stream weight estimation,proposed algorithm,theoretical result,decision fusion.,stream weight,robust speech recognition,stream classification problem,recognition task,recognition tasks,pattern recognition,classification algorithms,nonlinear function,reliability,speech,model error,speech recognition,automatic speech recognition,unsupervised learning,testing,speech processing,signal to noise ratio,indexing terms,machine learning | Speech processing,Nonlinear system,Data stream clustering,Information processing,Pattern recognition,Computer science,Speech recognition,Sensor fusion,Unsupervised learning,Artificial intelligence,Statistical classification,Computation | Journal |
Volume | Issue | ISSN |
17 | 3 | 1558-7916 |
Citations | PageRank | References |
2 | 0.38 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo Sánchez-Soto | 1 | 14 | 1.61 |
| Alexandros Potamianos | 2 | 1443 | 149.05 |
| Khalid Daoudi | 3 | 145 | 23.68 |