Name
Abstract | ||
|---|---|---|
In this paper, we study a novel approach to spoken language recognition using an ensemble of binary classifiers. In this framework, we begin by representing a speech utterance with a high-dimensional feature vector such as the phonotactic characteristics or the polynomial expansion of cepstral features. A binary classifier can be built based on such feature vectors. We adopt a distributed output coding strategy in ensemble classifier design, where we decompose a multiclass language recognition problem into many binary classification tasks, each of which addresses a language recognition subtask by using a component classifier. Then, we combine the results of the component classifiers to form an output code as a hypothesized solution to the overall language recognition problem. In this way, we effectively project high-dimensional feature vectors into a tractable low-dimensional space, yet maintaining language discriminative characteristics of the spoken utterances. By fusing the output codes from both phonotactic features and cepstral features, we achieve equal-error-rates of 1.38% and 3.20% for 30-s trials on the 2003 and 2005 NIST language recognition evaluation databases. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TASL.2007.902861 | IEEE Transactions on Audio, Speech & Language Processing |
Keywords | Field | DocType |
spoken language recognition,output codes,ensemble classifiers,high-dimensional feature vector,component classifier,language recognition,index terms,binary classifier,cepstral feature,output code,nist language recognition evaluation,multiclass language recognition problem,component classifier selection,language discriminative characteristic,language recognition subtask,binary classification,feature vector,vectors,natural language processing,speech coding,speech recognition,support vector machines,indexing terms,polynomial expansion,acoustics,encoding | Speech processing,Binary classification,Computer science,Utterance,Artificial intelligence,Natural language processing,Classifier (linguistics),Spoken language,Language model,Feature vector,Pattern recognition,Support vector machine,Speech recognition | Journal |
Volume | Issue | ISSN |
15 | 7 | 1558-7916 |
Citations | PageRank | References |
19 | 0.78 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Ma | 1 | 600 | 47.26 |
| Haizhou Li | 2 | 3678 | 334.61 |
| Rong Tong | 3 | 108 | 11.33 |