Browsing by Author "Wu, GD"

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A recurrent neural fuzzy network for word boundary detection in variable noise-level environments
(2001-02-01)
Wu, GD
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed. In fact, the background noise level mag vary during the procedure of recording. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level, In order to solve this problem, we first propose a refined time-frequency (RTF) parameter for extracting both the time and frequency features of noisy speech signals. The RTF parameter extends the (time-frequency) TF parameter proposed by Junqua et al, from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise clear. The RTF parameter can extract useful frequency information, Based on this RTF parameter, we further propose a new word boundary detection algorithm by using a recurrent sell-organizing neural fuzzy inference network (RSONFIN). Since RSONFIN can process the temporal relations, the proposed RTF-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level. As compared to normal neural networks, the RSONFIN can always find itself an economic network size with high-learning speed, Due to the self-learning ability of RSONFIN, this RTF-based RSONFIN algorithm avoids the need for empirically determining ambiguous decision rules in normal word boundary detection algorithms. Experimental results show that this new algorithm achieves higher recognition rate than the TF-based algorithm which has been shown to outperform several commonly used word boundary detection algorithms by about 12% in variable background noise level condition. It also reduces the recognition error rate due to endpoint detection to about 23%, compared to an average of 47% obtained by the TF-based algorithm in the same condition.
A robust word boundary detection algorithm for variable noise-level environment in cars
(2002-03-01)
Lin, CT
;
Lin, JY
;
Wu, GD
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper discusses the problem of automatic word boundary detection in the presence of variable-level background noise in cars. Commonly used robust word boundary detection algorithms always assume that the background noise level is fixed and sets fixed thresholds to rind the boundary of word signal. In fact, the background noise level in cars varies in the procedure of recording due to speed change and moving environment, and some thresholds should be tuned according to the variation of background noise level. This is the major reason that most robust word boundary detection algorithms cannot work well in the condition of variable background noise level. To solve this problem, we propose a minimum mel-scale frequency band (MiMSB) parameter which can estimate the varying background noise level in cars by adaptively choosing one band with minimum energy from the mel-scale frequency bank. With the MiMSB parameter, some preset thresholds used to find the boundary of word signal are no longer fixed in all the recording intervals. These thresholds will be tuned according to the MiMSB parameter. We also propose an enhanced time-frequency (ETF) parameter by extending the time-frequency (TF) parameter proposed by Junqua et al from single band to multiband spectrum analysis, where the frequency bands help to make the distinction between speech signal and noise. The ETF parameter can extract useful frequency information by choosing some bands of the mel-scale frequency bank. Based on the MiMSB and ETF parameters, we finally propose a new robust algorithm for word boundary detection in variable noise-level environment. The new algorithm has been tested over a variety of noise conditions in cars and has been found to perform well not only under variable background noise level condition, but also under fixed background noise level condition. The new robust algorithm using the MiMSB and ETF parameters achieved higher recognition rate than the TF-based robust algorithm, which has been shown to outperform several commonly used algorithms, by about 5% in variable background noise level condition. It also reduced the recognition error rate due to endpoint detection to 25%, compared to an average of 34% obtained with the TF-based robust algorithm.
New techniques on deformed image motion estimation and compensation
(1999-12-01)
Lin, CT
;
Wu, GD
;
Hsiao, SC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa, Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages of individual and keep their merits at the same time, The proposed system can optimally balance the amount of data rate in tno aspects (i.e., compensation error and motion information), Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied for the moving object recognition in image sequence.
Noisy speech segmentation with multiband analysis and recurrent neural fuzzy network
(2001)
Wu, GD
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper addresses the problem of automatic word boundary detection in the presence of variable-level background noise. Commonly used algorithms for word boundary detection always assume that the background noise level is fixed. In fact, the background noise level may vary during the procedure of recording. In order to solve this problem, we propose the RTF-MiFre-based RSONFIN (a recurrent neural fuzzy network) algorithm. Since the RTF and MiFre parameters can extract useful frequency energy and RSONFIN can process the temporal relations, this RTF-MiFre-based RSONFIN algorithm can find the variation of the background noise level and detect correct word boundaries in the presence of variable background noise level. Our experiment results have shown that the RTF-MiFre-based RSONFIN algorithm has good performance in the presence of variable background noise level presence.
Noisy speech segmentation/enhancement with multiband analysis and neural fuzzy networks
(2002-11-01)
Lin, CT
;
Wu, RC
;
Wu, GD
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
Word boundary detection with mel-scale frequency bank in noisy environment
(2000-09-01)
Wu, GD
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper addresses the problem of automatic word boundary detection in the presence of noise. We first propose an adaptive time-frequency (ATF) parameter for extracting both the time and frequency features of noisy speech signals. The ATF parameter extends the TF parameter proposed by Junqua et al. from Single band to multiband spectrum analysis, where the frequency bands help to make the distinction of speech and noise signals clear. The ATF parameter can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. The ATF parameter increased the recognition rate by about 3% of a TF-based robust algorithm which has been shown to outperform several commonly used algorithms for word boundary detection in the presence of noise. The ATF parameter also reduced the recognition error rate due to endpoint detection to about 20%. Based on the ATF parameter, we further propose a new word boundary detection algorithm by using a neural fuzzy network (called SONFIN) for identifying islands of word signals in noisy environment. Due to the self-learning ability of SONFIN, the proposed algorithm avoids the need of empirically determining thresholds and ambiguous rules in normal word boundary detection algorithms. As compared to normal neural networks, the SONFIN can always find itself an economic network size in high learning speed. Our results also showed that the SONFIN's performance is not significantly affected by the size of training set. The ATF-based SONFIN achieved higher recognition rate than the TF-based robust algorithm by about 5%. It also reduced the recognition error rate due to endpoint detection to about 10%, compared to an average of approximately 30% obtained with the TF-based robust algorithm, and 50% obtained with the modified version of the Lamel ct al. algorithm.