### 摘要

A decomposition of signal into a set of frequency channels of equal bandwidth on a logarithmic scale, i.e., an analysis of the signal using constant Q filters, using wavelet and multiresolution analysis is used in this paper to derive cepstrum features of different spatial frequency bands. Based on the decompositions, each channel is modeled as a Bayesian subnetwork and each subnetwork is weighted by a weighting algorithm. The distortions for speech recognition between a reference model and the input vectors are then computed by summing the weighted scores of all decomposed channels. The experimental results show that the recognition rate of this method is superior to those non-weighting methods.

原文 | English |
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頁面 | 519-523 |

頁數 | 5 |

出版狀態 | Published - 1994 十二月 1 |

事件 | Proceedings of the 1994 IEEE Asia-Pacific Conference on Circuits and Systems - Taipei, Taiwan 持續時間: 1994 十二月 5 → 1994 十二月 8 |

### Other

Other | Proceedings of the 1994 IEEE Asia-Pacific Conference on Circuits and Systems |
---|---|

城市 | Taipei, Taiwan |

期間 | 94-12-05 → 94-12-08 |

### 指紋

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

### 引用此文

*Channel-weighting method for speech recognition using wavelet decompositions*. 519-523. 論文發表於 Proceedings of the 1994 IEEE Asia-Pacific Conference on Circuits and Systems, Taipei, Taiwan, .

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**Channel-weighting method for speech recognition using wavelet decompositions.** / Shyuu, Jyh Shing; Wang, Jhing Fa; Wu, Chung-Hsien.

研究成果: Paper

TY - CONF

T1 - Channel-weighting method for speech recognition using wavelet decompositions

AU - Shyuu, Jyh Shing

AU - Wang, Jhing Fa

AU - Wu, Chung-Hsien

PY - 1994/12/1

Y1 - 1994/12/1

N2 - A decomposition of signal into a set of frequency channels of equal bandwidth on a logarithmic scale, i.e., an analysis of the signal using constant Q filters, using wavelet and multiresolution analysis is used in this paper to derive cepstrum features of different spatial frequency bands. Based on the decompositions, each channel is modeled as a Bayesian subnetwork and each subnetwork is weighted by a weighting algorithm. The distortions for speech recognition between a reference model and the input vectors are then computed by summing the weighted scores of all decomposed channels. The experimental results show that the recognition rate of this method is superior to those non-weighting methods.

AB - A decomposition of signal into a set of frequency channels of equal bandwidth on a logarithmic scale, i.e., an analysis of the signal using constant Q filters, using wavelet and multiresolution analysis is used in this paper to derive cepstrum features of different spatial frequency bands. Based on the decompositions, each channel is modeled as a Bayesian subnetwork and each subnetwork is weighted by a weighting algorithm. The distortions for speech recognition between a reference model and the input vectors are then computed by summing the weighted scores of all decomposed channels. The experimental results show that the recognition rate of this method is superior to those non-weighting methods.

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UR - http://www.scopus.com/inward/citedby.url?scp=0028740144&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0028740144

SP - 519

EP - 523

ER -