{"title":"Segment-based speaker adaptation by neural network","authors":"K. Fukuzawa, H. Sawai, M. Sugiyama","doi":"10.1109/NNSP.1991.239497","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239497","url":null,"abstract":"The authors propose a segment-to-segment speaker adaptation technique using a feed-forward neural network with a time shifted sub-connection architecture. Differences in voice individuality exist in both the spectral and temporal domains. It is generally known that frame based speaker adaptation techniques can not compensate for speaker individuality in the temporal domain. Segment based speaker adaptation compensates for these spectral and temporal differences. The results of 23 Japanese phoneme recognition experiments using TDNN (time-delay neural network) show that the recognition rate by segment-based adaptations was 83.7%, 22.8% higher than the rate without adaptation.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131812355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Vector quantization of images using neural networks and simulated annealing","authors":"M. Lech, Y. Hua","doi":"10.1109/NNSP.1991.239486","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239486","url":null,"abstract":"Vector quantization (VQ) has already been established as a very powerful data compression technique. Specification of the 'codebook', which contains the best possible collection of 'codewords', effectively representing the variety of source vectors to be encoded is one of the most critical requirements of VQ systems, and belongs, for most applications, to the class of hard optimization problems. A number of new approaches to codebook generation methods using neural networks (NN) and simulated annealing (SA) are presented and compared. The authors discuss the competitive learning algorithm (CL) and Kohonen's self-organizing feature maps (KSFM). The algorithms are examined using a new training rule and comparisons with the standard rule is included. A new solution to the problem of determining the 'closest' neural unit is also proposed. The second group of methods considered are all based on simulated annealing (SA). A number of improvements to and alternative constructions of the classical 'single path' simulated annealing algorithm are presented to address the problem of suboptimality of VQ codebook generation and provide methods by which solutions closer to the optimum are obtainable for similar computational effort.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131503399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A relaxation neural network model for optimal multi-level image representation by local-parallel computations","authors":"N. Sonehara","doi":"10.1109/NNSP.1991.239494","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239494","url":null,"abstract":"A relaxation neural network model is proposed to solve the multi-level image representation problem by energy minimization in local and parallel computations. This network iteratively minimizes the computational energy defined by the local error in neighboring picture elements. This optimization method can generate high quality binary and multi-level images depending on local features, and can be implemented efficiently on parallel computers.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117167844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Edge detection for optical image metrology using unsupervised neural network learning","authors":"H. Aghajan, C. Schaper, T. Kailath","doi":"10.1109/NNSP.1991.239523","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239523","url":null,"abstract":"Several unsupervised neural network learning methods are explored and applied to edge detection of microlithography optical images. Lack of a priori knowledge about correct state assignments for learning procedure in optical microlithography environment makes the metrology problem a suitable area for applying unsupervised learning strategies. The methods studied include a self-organizing competitive learner, a bootstrapped linear threshold classifier, and a constrained maximization algorithm. The results of the neural network classifiers were compared to the results obtained by a standard straight edge detector based on the Radon transform and good consistency was observed in the results together with superiority in speed for the neural network classifiers. Experimental results are presented and compared with measurements obtained via scanning electron microscopy.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"445 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125772173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving learning rate of neural tree networks using thermal perceptrons","authors":"Ananth Sankar, R. Mammone","doi":"10.1109/NNSP.1991.239532","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239532","url":null,"abstract":"A new neural network called the neural tree network (NTN) is a combination of decision trees and multi-layer perceptrons (MLP). The NTN grows the network as opposed to MLPs. The learning algorithm for growing NTNs is more efficient that standard decision tree algorithms. Simulation results have shown that the NTN is superior in performance to both decision trees and MLPs. A new NTN learning algorithm is proposed based on the thermal perceptron algorithm. It is shown that the new algorithm greatly increases the speed of learning of the NTN and attains similar classification performance as the previously used algorithm.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131002598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Zavaliagkos, S. Austin, J. Makhoul, R. Schwartz
{"title":"A hybrid continuous speech recognition system using segmental neural nets with hidden Markov models","authors":"G. Zavaliagkos, S. Austin, J. Makhoul, R. Schwartz","doi":"10.1109/NNSP.1991.239507","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239507","url":null,"abstract":"The authors present the concept of a 'segmental neural net' (SNN) for phonetic modeling in continuous speech recognition (CSR) and demonstrate how than can be used with a multiple hypothesis (or N-Best) paradigm to combine different CSR systems. In particular, they have developed a system that combines the SNN with a hidden Markov model (HMM) system. They believe that this is the first system incorporating a neural network for which the performance has exceeded the state of the art in large-vocabulary, continuous speech recognition. To take advantage of the training and decoding speed of HMMs, the authors have developed a novel hybrid SNN/HMM system that combines the advantages of both types of approaches. In this hybrid system, use is made of the N-best paradigm to generate likely phonetic segmentations, which are then scored by the SNN. The HMM and SNN scores are then combined to optimize performance.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133057733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learned representation normalization: attention focusing with multiple input modules","authors":"M. L. Rossen","doi":"10.1109/NNSP.1991.239530","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239530","url":null,"abstract":"A large, multi-modular neural network can be envisaged for use in a complex, multi-task application. The optimum data representation for each sub-task of such an application is often unknown and different from the optimum data representation for the other sub-tasks. A method is needed that allows a network that contains several alternate input representations to learn to focus its attention on the best representation(s) for each sub-task to be learned, without a priori information on best representation-sub-task combinations. An adaptive attention focusing method is introduced that addresses this issue. The method involves training recurrent connections for each input module to selectively attenuate input to that module that causes training error in a final target module. The method is shown to have similarities with both gating networks and anti-Hebbian learning. A task scenario is proposed for which adaptive attention focusing provides superior classification performance relative to standard training methods.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116172878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Restricted learning algorithm and its application to neural network training","authors":"T. Miyamura, I. Yamada, K. Sakaniwa","doi":"10.1109/NNSP.1991.239528","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239528","url":null,"abstract":"The authors propose a new (semi)-optimization algorithm, called the restricted learning algorithm, for a nonnegative evaluating function which is 2 times continuously differentiable on a compact set Omega in R/sup N/. The restricted learning algorithm utilizes the maximal excluding regions which are newly derived, and is shown to converge to the global in -optimum in Omega . A most effective application of the proposed algorithm is the training of multi-layered neural networks. In this case, one can estimate the Lipschitz's constants for the evaluating function and its derivative very efficiently and thereby we can obtain sufficiently large excluding regions. It is confirmed through numerical examples that the proposed restricted learning algorithm provides much better performance than the conventional back propagation algorithm and its modified versions.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130326846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A space-perturbance/time-delay neural network for speech recognition","authors":"J. Ming, Chen Huihuang, S. Zhenkang","doi":"10.1109/NNSP.1991.239503","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239503","url":null,"abstract":"The authors present a space-perturbance time-delay neural network (SPTDNN), which is a generalization of the time-delay neural network (TDNN) approach. It is shown that by introducing the space-perturbance arrangement, the SPTDNN has the ability to be robust to both temporal and dynamic acoustic variance of speech features, thus, is a potentially component approach to speaker-independent and/or noisy speech recognition. The authors introduce the architecture, learning algorithm, and theoretical evaluation of the SPTDNN, along with experimental results. Experimental comparisons show that the SPTDNN obtains a performance that improves upon the TDNN for both speaker-dependent/-independent and noisy phoneme recognition.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132106976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Speech recognition by combining pairwise discriminant time-delay neural networks and predictive LR-parser","authors":"J. Takami, A. Kai, S. Sagayama","doi":"10.1109/NNSP.1991.239509","DOIUrl":"https://doi.org/10.1109/NNSP.1991.239509","url":null,"abstract":"A phoneme recognition method using pairwise discriminant time-delay neural networks (PD-TDNNs) and a continuous speech recognition method using the PD-TDNNs are proposed. It is shown that classification-type neural networks have poor robustness against the difference in speaking rates between training data and testing data. To improve the robustness, the authors developed a phoneme recognition method using PD-TDNNs. This method has high performance owing to its particular mechanism, that is a majority decision by multiple less sharp discrimination boundaries. They tested these methods on both consonant recognition and phrase recognition, and obtained higher recognition performance compared with a conventional method using a single TDNN.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116792347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}