| Title: | A comprehensive noise robust speech parameterization algorithm using wavelet packet decomposition-based denoising and speech feature representation techniques |
|---|
| Authors: | ID Kotnik, Bojan (Author)
ID Kačič, Zdravko (Author) |
|---|
| Files: |  EURASIP_Journal_on_Advances_in_Signal_Processing_2007_Kotnik,_Kacic_A_Comprehensive_Noise_Robust_Speech_Parameterization_Algorithm_Using.pdf (984,48 KB)
MD5: B11834830317136189B6F90E818F907E
 https://asp-eurasipjournals.springeropen.com/articles/10.1155/2007/64102
|
|---|
| Language: | English |
|---|
| Work type: | Scientific work |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| Abstract: | This paper concerns the problem of automatic speech recognition in noise-intense and adverse environments. The main goal of the proposed work is the definition, implementation, and evaluation of a novel noise robust speech signal parameterization algorithm. The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition. A new modified soft thresholding algorithm based on time-frequency adaptive threshold determination was developed to efficiently reduce the level of additive noise in the input noisy speech signal. A two-stage Gaussian mixture model (GMM)-based classifier was developed to perform speech/nonspeech as well as voiced/unvoiced classification. The adaptive topology of the wavelet packet decomposition tree based on voiced/unvoiced detection was introduced to separately analyze voiced and unvoiced segments of the speech signal. The main feature vector consists of a combination of log-root compressed wavelet packet parameters, and autoregressive parameters. The final output feature vector is produced using a two-staged feature vector postprocessing procedure. In the experimental framework, the noisy speech databases Aurora 2 and Aurora 3 were applied together with corresponding standardized acoustical model training/testing procedures. The automatic speech recognition performance achieved using the proposed noise robust speech parameterization procedure was compared to the standardized mel-frequency cepstral coefficient (MFCC) feature extraction procedures ETSI ES 201 108 and ETSI ES 202 050. |
|---|
| Keywords: | speech parametrization, algorithm, speech techniques |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Year of publishing: | 2007 |
|---|
| Number of pages: | str. 1-20 |
|---|
| Numbering: | Letn. 2007 |
|---|
| PID: | 20.500.12556/DKUM-66439  |
|---|
| ISSN: | 1687-6172 |
|---|
| UDC: | 004.9 |
|---|
| ISSN on article: | 1687-6172 |
|---|
| COBISS.SI-ID: | 11385622 |
|---|
| DOI: | 10.1155/2007/64102 |
|---|
| NUK URN: | URN:SI:UM:DK:IYT9PUUY |
|---|
| Publication date in DKUM: | 26.06.2017 |
|---|
| Views: | 1707 |
|---|
| Downloads: | 406 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
