6989862
Using Prosody for Automatic Sentence Segmentation of Multi-party Meetings
We explore the use of prosodic features beyond pauses, including duration, pitch, and energy features, for automatic sentence segmentation of ICSI meeting data We examine two different approaches to boundary classification: score-level combination of independent language and prosodic models using HMMs, and feature-level combination of models using a boosting-based method (BoosTexter) We report classification results for reference word transcripts as well as for transcripts from a state-of-the-art automatic speech recognizer (ASR) We also compare results using the lexical model plus a pause-only prosody model, versus results using additional prosodic features Results show that (1) information from pauses is important, including pause duration both at the boundary and at the previous and following word boundaries; (2) adding duration, pitch, and energy features yields significant improvement over pause alone; (3) the integrated boosting-based model performs better than the HMM for ASR conditions; (4) training the boosting-based model on recognized words yields further improvement.
9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
6898492
Using Conditional Random Fields for Sentence Boundary Detection in Speech
Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a conditional random field (CRF) for this task and relate results with this model to our prior work. We evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. In general, our CRF model yields a lower error rate than the HMM and Maxent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. This probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources.
15370058
Evolutionary programming made faster
Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In the paper, a "fast EP" (FEP) is proposed which uses a Cauchy instead of Gaussian mutation as the primary search operator. The relationship between FEP and classical EP (CEP) is similar to that between fast simulated annealing and the classical version. Both analytical and empirical studies have been carried out to evaluate the performance of FEP and CEP for different function optimization problems. The paper shows that FEP is very good at search in a large neighborhood while CEP is better at search in a small local neighborhood. For a suite of 23 benchmark problems, FEP performs much better than CEP for multimodal functions with many local minima while being comparable to CEP in performance for unimodal and multimodal functions with only a few local minima. The paper also shows the relationship between the search step size and the probability of finding a global optimum and thus explains why FEP performs better than CEP on some functions but not on others. In addition, the importance of the neighborhood size and its relationship to the probability of finding a near-optimum is investigated. Based on these analyses, an improved FEP (IFEP) is proposed and tested empirically. This technique mixes different search operators (mutations). The experimental results show that IFEP performs better than or as well as the better of FEP and CEP for most benchmark problems tested.
6802371
Improving web search results using affinity graph
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
6112726
Pseudo Trust: Zero-Knowledge Based Authentication in Anonymous Peer-to-Peer Protocols
Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called pseudo trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on zero-knowledge proof is designed so peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the levels of security and anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype implementation. The strengths of pseudo trust include the lack of need for a centralized trusted party or CA, high scalability and security, low traffic and cryptography processing overheads, and man-in-middle attack resistance. We aim for the pseudo trust design to be included in the P2P trust and anonymity context.
14379760
Protein Quaternary Fold Recognition Using Conditional Graphical Models
Protein fold recognition is a crucial step in inferring biological structure and function. This paper focuses on machine learning methods for predicting quaternary structural folds, which consist of multiple protein chains that form chemical bonds among side chains to reach a structurally stable domain. The complexity associated with modeling the quaternary fold poses major theoretical and computational challenges to current machine learning methods. We propose methods to address these challenges and show how (1) domain knowledge is encoded and utilized to characterize structural properties using segmentation conditional graphical models; and (2) model complexity is handled through efficient inference algorithms. Our model follows a discriminative approach so that any informative features, such as those representative of overlapping or long-range interactions, can be used conveniently. The model is applied to predict two important quaternary folds, the triple β-spirals and double-barrel trimers. Cross-family validation shows that our method outperforms other state-of-the art algorithms.
9019278
Querying Complex Graphs
This paper presents a powerful language for querying complex graphs and a method for generating efficient implementations that can answer queries with complexity guarantees. The graphs may have edge labels that may have parameters, and easily and naturally capture complex interrelated objects in object-oriented systems and XML data. The language is built on extended regular path expressions with variables and scoping, and can express queries more easily and clearly than previous query languages.The method for implementation first transforms queries into Datalog with limited extensions. It then extends a previous method to generate specialized algorithms and complexity formulas from Datalog with these extensions.