33544047
Isolated Word Recognition by Neural Network Models with Cross-Correlation Coefficients for Speech Dynamics
This paper presents an artificial neural network (ANN) for speaker-independent isolated word speech recognition. The network consists of three subnets in concatenation. The static information within one frame of speech signal is processed in the probabilistic mapping subnet that converts an input vector of acoustic features into a probability vector whose components are estimated probabilities of the feature vector belonging to the phonetic classes that constitute the words in the vocabulary. The dynamics capturing subnet computes the first-order cross correlation between the components of the probability vectors to serve as the discriminative feature derived from the interframe temporal information of the speech signal. These dynamic features are passed for decision-making to the classification subnet, which is a multilayer perceptron (MLP). The architecture of these three subnets are described, and the associated adaptive learning algorithms are derived. The recognition results for a subset of the DARPA TIMIT speech database are reported. The correct recognition rate of the proposed ANN system is 95.5%, whereas that of the best of continuous hidden Markov model (HMM)-based systems is only 91.0%. >
7674871
Contextual vector quantization for speech recognition with discrete hidden Markov model
Abstract By using formulation of the finite mixture distribution identification, in this paper, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The motivation to use MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition.
18926397
Chinese Word Segmentation based on Maximum Matching and Word Binding Force
A Chinese word segmentation algorithm based on forward maximum matching and word binding force is proposed in this paper. This algorithm plays a key role in post-processing the output of a character or speech recognizer in determining the proper word sequence corresponding to an input line of character images or a speech waveform. To support this algorithm, a text corpus of over 63 millions characters is employed to enrich an 80,000-words lexicon in terms of its word entries and word binding forces. As it stands now, given an input line of text, the word segmentor can process on the average 210,000 characters per second when running on an IBM RISC System/6000 3BT workstation with a correct word identification rate of 99.74%.
12408507
A Rough Set Approach to Attribute Generalization in Data Mining
Abstract This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory.
5780401
Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function
MOTIVATIONS
Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function.


RESULTS
Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
206954852
A feedback control circuit design technique to suppress power noise in high speed output driver
In today's sub-micron CMOS integrated circuit technology, high speed output switching signals interacting with external inductance and capacitance produce noise which contaminates output signals and power buses. A Feedback Control Slew Rate Output Driver (FCSROD) which reduces the noise spike down to approximately 64% of a conventional output buffer without incurring the penalty of the propagation delay and even the rise/fall time is described. This effective power noise suppression is achieved by using distributed and weighted switching driver segments in conjunction with feedback control to control the output driver's slew rate. Dynamic short circuit current which is generated while both pFET and nFET are conducting is also minimized to reduce di/dt noise. FCSROD was compared with a conventional and the controlled slew rate output buffer, showing 64% noise reduction comparing to the conventional driver, and 22% improvement in both propagation delay and rise/fall time comparing with the controlled slew rate output driver.
206413943
Test Generation with Dynamic Probe Points in High Observability Testing Environment
High observability testing environment allows internal circuit nodes to be used as test points. However, such flexibility requires the development of new ATPG algorithm. Previous reported algorithm does not guarantee full fault-coverage and assumes all internal circuit nodes are test points. The new algorithm described in this paper will generate a full fault-coverage test set for a fanout free combinational circuit. The main characteristic of the algorithm is that it generates test vectors as well as probe points. As a result, the probe points are different for each test vector, and the number of probe points is the minimum for test set generated. Results obtained show that an average of 30% test vector reduction is achieved compared with the conventional test method which uses only output pins as test points.