Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition
<p/> <p>This paper presents a novel model compensation (MC) method for the features of mel-frequency cepstral coefficients (MFCCs) with signal-to-noise-ratio- (SNR-) dependent nonuniform spectral compression (SNSC). Though these new MFCCs derived from a SNSC scheme have been shown to be...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2007-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2007/032546 |
| _version_ | 1811278803683508224 |
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| author | Ning Geng-Xin Wei Gang Chu Kam-Keung |
| author_facet | Ning Geng-Xin Wei Gang Chu Kam-Keung |
| author_sort | Ning Geng-Xin |
| collection | DOAJ |
| description | <p/> <p>This paper presents a novel model compensation (MC) method for the features of mel-frequency cepstral coefficients (MFCCs) with signal-to-noise-ratio- (SNR-) dependent nonuniform spectral compression (SNSC). Though these new MFCCs derived from a SNSC scheme have been shown to be robust features under matched case, they suffer from serious mismatch when the reference models are trained at different SNRs and in different environments. To solve this drawback, a compressed mismatch function is defined for the static observations with nonuniform spectral compression. The means and variances of the static features with spectral compression are derived according to this mismatch function. Experimental results show that the proposed method is able to provide recognition accuracy better than conventional MC methods when using uncompressed features especially at very low SNR under different noises. Moreover, the new compensation method has a computational complexity slightly above that of conventional MC methods.</p> |
| first_indexed | 2024-04-13T00:42:07Z |
| format | Article |
| id | doaj.art-732634d3f914426ab5ed3af5d747d09b |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-04-13T00:42:07Z |
| publishDate | 2007-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-732634d3f914426ab5ed3af5d747d09b2022-12-22T03:10:06ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-0120071032546Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech RecognitionNing Geng-XinWei GangChu Kam-Keung<p/> <p>This paper presents a novel model compensation (MC) method for the features of mel-frequency cepstral coefficients (MFCCs) with signal-to-noise-ratio- (SNR-) dependent nonuniform spectral compression (SNSC). Though these new MFCCs derived from a SNSC scheme have been shown to be robust features under matched case, they suffer from serious mismatch when the reference models are trained at different SNRs and in different environments. To solve this drawback, a compressed mismatch function is defined for the static observations with nonuniform spectral compression. The means and variances of the static features with spectral compression are derived according to this mismatch function. Experimental results show that the proposed method is able to provide recognition accuracy better than conventional MC methods when using uncompressed features especially at very low SNR under different noises. Moreover, the new compensation method has a computational complexity slightly above that of conventional MC methods.</p>http://asp.eurasipjournals.com/content/2007/032546 |
| spellingShingle | Ning Geng-Xin Wei Gang Chu Kam-Keung Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition EURASIP Journal on Advances in Signal Processing |
| title | Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition |
| title_full | Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition |
| title_fullStr | Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition |
| title_full_unstemmed | Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition |
| title_short | Model Compensation Approach Based on Nonuniform Spectral Compression Features for Noisy Speech Recognition |
| title_sort | model compensation approach based on nonuniform spectral compression features for noisy speech recognition |
| url | http://asp.eurasipjournals.com/content/2007/032546 |
| work_keys_str_mv | AT ninggengxin modelcompensationapproachbasedonnonuniformspectralcompressionfeaturesfornoisyspeechrecognition AT weigang modelcompensationapproachbasedonnonuniformspectralcompressionfeaturesfornoisyspeechrecognition AT chukamkeung modelcompensationapproachbasedonnonuniformspectralcompressionfeaturesfornoisyspeechrecognition |