6880755
Implicit Trajectory Modeling through Gaussian Transition Models for Speech Recognition
It is well known that frame independence assumption is a fundamental limitation of current HMM based speech recognition systems. By treating each speech frame independently, HMMs fail to capture trajectory information in the acoustic signal. This paper introduces Gaussian Transition Models (GTM) to model trajectories implicitly. Comparing to alternative approaches, such as segment modeling and parallel path HMM, GTM has the advantage that it integrates seamlessly with the HMM frame-work; it can model a large number of trajectories and there is no need to define a topology a priori. Preliminary experiments on Switchboard, a large vocabulary conversational speech recognition task, have shown promising results.
9764475
Towards a multimodal meeting record
Face-to-face meetings usually encompass several modalities including speech, gesture, handwriting, and person identification. Recognition and integration of each of these modalities is important to create an accurate record of a meeting. However, each of these modalities presents recognition difficulties. Speech recognition must be speaker and domain independent, have low word error rates, and be close to real time to be useful. Gesture and handwriting recognition must be writer independent and support a wide variety of writing styles. Person identification has difficulty with segmentation in a crowded room. Furthermore, in order to produce the record automatically, we have to solve the assignment problem (who is saying what), which involves people identification and speech recognition. This paper examines a multimodal meeting room system under development at Carnegie Mellon University that enables us to track, capture and integrate the important aspects of a meeting from people identification to meeting transcription. Once a multimedia meeting record is created, it can be archived for later retrieval.
7168030
Minimal skew clock embedding considering time variant temperature gradient
The existing temperature-aware clock embedding assumes a time-invariant temperature gradient. However, it is not solved how to find the worst-case temperature gradient leading to the worst case skew. In this paper, we develop a PErturbation based Clock Optimization(PECO) considering the time-variant temperature gradient. For a given clock topology, we minimizethe worst case skew without asking for the worst case temperature map. We decide the merging point level by level based on the sensitivity of the skew with respect to the change of merging point. Such sensitivity is calculated using a parameterized model, which is compressed by a singular-value-decomposition (SVD) and K-meansbased clustering considering the temperature correlation. The experimental results show that our algorithm reduces worst-case skew by up to 5X compared to the existing zero skew based ZST/DME method with small (up to 1%) wirelength overhead.
22691758
A neural network for 500 word vocabulary word spotting using non-uniform units
We introduce acoustic sub-word units to neural networks for speaker-independent continuous speech recognition. The functions of segmenting input and detecting words are implemented with networks of simple structures. The non-uniform unit which we introduce in this research can model phoneme variations caused by co-articulation spread over several phonemes and between words. These units can be segmented by the network according to stationary and transition parts of speech without iteration or without considering all possible position shifts. A word lexicon can be trained by the network, which can effectively memorize all transcription variations in the training utterances of words. The results of speaker-independent word spotting of 520 words with TIMIT data are described.
14421655
Signed quorum systems
With n servers that independently fail with probability of p < 0.5, it is well known that the majority quorum system achieves the best availability among all quorum systems. However, even this optimal construction requires (n+1)/2 functioning servers out of n. Furthermore, the number of probes needed to acquire a quorum is also lower bounded by (n+1)/2.Motivated by the need for a highly available and low probe complexity quorum system in the Internet, this paper proposes signed quorum systems (SQS) that can be available as long as any O(1) servers are available, and simultaneously have O(1) probe complexity. SQS provides probabilistic intersection guarantees and exploits the property of independent mismatches in today's Internet. Such key property has been validated previously under multiple Internet measurement traces. This paper then extensively studies the availability, probe complexity, and load of SQS, derives lower bounds for all three metrics, and constructs matching upper bounds. We show that in addition to the qualitatively superior availability and probe complexity, SQS also decouples availability from load and probe complexity, so that optimal availability can be achieved under most probe complexity and load values.
40372826
Caching and Concurrency Control in Mobile Client/Server Database Systems
303600
Predicting interactions in protein networks by completing defective cliques
UNLABELLED
Datasets obtained by large-scale, high-throughput methods for detecting protein-protein interactions typically suffer from a relatively high level of noise. We describe a novel method for improving the quality of these datasets by predicting missed protein-protein interactions, using only the topology of the protein interaction network observed by the large-scale experiment. The central idea of the method is to search the protein interaction network for defective cliques (nearly complete complexes of pairwise interacting proteins), and predict the interactions that complete them. We formulate an algorithm for applying this method to large-scale networks, and show that in practice it is efficient and has good predictive performance. More information can be found on our website http://topnet.gersteinlab.org/clique/


CONTACT
Mark.Gerstein@yale.edu


SUPPLEMENTARY INFORMATION
Supplementary Materials are available at Bioinformatics online.