Name
Abstract | ||
|---|---|---|
This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSA.2005.848885 | Speech and Audio Processing, IEEE Transactions |
Keywords | Field | DocType |
cepstral analysis,maximum likelihood estimation,pattern classification,phase space methods,speech recognition,time-domain analysis,composite classifier,maximum likelihood recognition,mel-frequency cepstral baseline system,nonlinear systems,reconstructed phase spaces,speech phoneme waveforms,speech recognition,time-domain isolated phoneme classification,underlying production system,Nonlinear systems,phoneme classification,reconstructed phase space,speech recognition | Time domain,Speech processing,TIMIT,Pattern recognition,Computer science,Cepstrum,Phase space,Speech recognition,Heuristics,Artificial intelligence,Statistical model,Classifier (linguistics) | Journal |
Volume | Issue | ISSN |
13 | 4 | 1063-6676 |
Citations | PageRank | References |
23 | 1.31 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael T. Johnson | 1 | 435 | 53.51 |
| Richard J. Povinelli | 2 | 225 | 20.40 |
| Andrew C. Lindgren | 3 | 79 | 4.99 |
| Jinjin Ye | 4 | 96 | 6.41 |
| Xiaolin Liu | 5 | 23 | 1.31 |
| Kevin M. Indrebo | 6 | 38 | 2.87 |