32652355
Browsing the Chinese Web Pages Using Mandarin Speech
A speech interface that allows easy access to information on the WWW has the potential to make the browser more friendly and powerful. This paper, thus, presents a working Mandarin speech interface for using unconstrained Mandarin speech to control the WWW browser for conveniently browsing the Chinese Web pages. The interface currently provides speakable commands, bookmarks, and links. The experimental results show that our approach which specially consider the characteristics of the Chinese language is very effective, and very high accuracy can be achieved.
24553
Mandarin-English Information (MEI): investigating translingual speech retrieval
Abstract This paper describes the Mandarin–English Information (MEI) project, where we investigated the problem of cross-language spoken document retrieval (CL-SDR), and developed one of the first English–Chinese CL-SDR systems. Our system accepts an entire English news story (text) as query, and retrieves relevant Chinese broadcast news stories (audio) from the document collection. Hence, this is a cross-language and cross-media retrieval task. We applied a multi-scale approach to our problem, which unifies the use of phrases, words and subwords in retrieval. The English queries are translated into Chinese by means of a dictionary-based approach, where we have integrated phrase-based translation with word-by-word translation. Untranslatable named entities are transliterated by a novel subword translation technique. The multi-scale approach can be divided into three subtasks – multi-scale query formulation, multi-scale audio indexing (by speech recognition) and multi-scale retrieval. Experimental results demonstrate that the use of phrase-based translation and subword translation gave performance gains, and multi-scale retrieval outperforms word-based retrieval.
2984995
Query-By-Example Technique for Retrieving Cover Versions of Popular Songs with Similar Melodies
Retrieving audio material based on audio queries is an important and challenging issue in the research field of content-based access to popular music. As part of this research field, we present a preliminary investigation into retrieving cover versions of songs specified by users. The technique enables users to listen to songs with an identical tune, but performed by different singers, in different languages, genres, and so on. The proposed system is built on a query-by-example framework, which takes a fragment of the song submitted by the user as input, and returns songs similar to the query in terms of the main melody as output. To handle the likely discrepancies, e.g., tempos, transpositions, and accompaniments between cover versions and the original song, methods are presented to remove the non-vocal portions of the song, extract the sung notes from the accompanied vocals, and compare the similarities between the sung note sequences.
10755440
Syllable-Based Chinese Text/Spoken Document Retrieval Using Text/Speech Queries
In light of the rapid growth of Chinese information resources on the Internet, this study investigates a novel approach that deals with the problem of Chinese text and spoken document retrieval using both text and speech queries. By properly utilizing the monosyllabic structure of the Chinese language, the proposed approach estimates the statistical similarity between the text/speech queries and the text/spoken documents at the phonetic level using the syllable-based statistical information. The investigation successfully implemented a prototype system with an interface supporting some user-friendly functions and the initial test results demonstrate the feasibility of the proposed approach.
12580255
Comparison of Word and Subword Indexing Techniques for Mandarin Chinese Spoken Document Retrieval
In this paper, we investigate the use of words and subwords (including both characters and syllables) in audio indexing for Mandarin Chinese spoken document retrieval. Two retrieval approaches, including the well-known vector space model approach and the newly proposed HMM/Ngram-based approach, are used in the present work. We focus on the use of an entire Chinese textual story (from a newspaper) as a query to retrieve Mandarin Chinese spoken documents (from news broadcasts). Experiments are based on the Topic Detection and Tracking Corpora.
34060618
A Minimum Boundary Error Framework for Automatic Phonetic Segmentation
This paper presents a novel framework for HMM-based automatic phonetic segmentation that improves the accuracy of placing phone boundaries. In the framework, both training and segmentation approaches are proposed according to the minimum boundary error (MBE) criterion, which tries to minimize the expected boundary errors over a set of possible phonetic alignments. This framework is inspired by the recently proposed minimum phone error (MPE) training approach and the minimum Bayes risk decoding algorithm for automatic speech recognition. To evaluate the proposed MBE framework, we conduct automatic phonetic segmentation experiments on the TIMIT acoustic-phonetic continuous speech corpus. MBE segmentation with MBE-trained models can identify 80.53% of human-labeled phone boundaries within a tolerance of 10 ms, compared to 71.10% identified by conventional ML segmentation with ML-trained models. Moreover, by using the MBE framework, only 7.15% of automatically labeled phone boundaries have errors larger than 20 ms.
15118493
Anomaly intrusion detection using multi-objective genetic fuzzy system and agent-based evolutionary computation framework
In this paper, we present a multi-objective genetic fuzzy system for anomaly intrusion detection. The proposed system extracts accurate and interpret able fuzzy rule-based knowledge from network data using an agent-based evolutionary computation framework. The experimental results on KDD-Cup99 intrusion detection benchmark data demonstrate that our system can achieve high detection rate for intrusion attacks and low false positive rate for normal network traffic.
31566264
Cooperation-Based Behavior Design
Robot soccer explores such a research topic that multiple agents work together in a Real-time, Cooperative and Adversarial (RCA) environment to achieve specific objectives. It requires that each agent can not only deal with situations individually, but also present cooperation with its teammates. In this paper, we describe a robot architecture, which addresses "scaling cooperation" among robots, and meanwhile allows each robot to make decisions independently in real-time case. The architecture is based on "ideal cooperation" principle and implemented for Small Robot League in RoboCup. Experimental results prove its effectiveness and reveal several primary characteristics of behaviors in robot soccer.
5230665
Finding global icebergs over distributed data sets
Finding icebergs–items whose frequency of occurrence is above a certain threshold–is an important problem with a wide range of applications. Most of the existing work focuses on iceberg queries at a single node. However, in many real-life applications, data sets are distributed across a large number of nodes. Two naïve approaches might be considered. In the first, each node ships its entire data set to a central server, and the central server uses single-node algorithms to find icebergs. But it may incur prohibitive communication overhead. In the second, each node submits local icebergs, and the central server combines local icebergs to find global icebergs. But it may fail because in many important applications, globally frequent items may not be frequent at any node. In this work, we propose two novel schemes that provide accurate and efficient solutions to this problem: a sampling-based scheme and a counting-sketch-based scheme. In particular, the latter scheme incurs a communication cost at least an order of magnitude smaller than the naïve scheme of shipping all data, yet is able to achieve very high accuracy. Through rigorous theoretical and experimental analysis we establish the statistical properties of our proposed algorithms, including their accuracy bounds.
8697079
Input don't care sequences in FSM networks
We present an approach to compute all input don't care sequences for a component in an FSM network with an arbitrary topology. In a cascade FSM network, Kim and Newborn's (K-N) procedure [13] exactly computes all input don't care sequences for the driven machine. However, for a component in a general FSM network the exact computation of input don't care sequences is unsolved. We demonstrate that this problem can be reduced to one for a cascade circuit. This reduction uses the notion ofan abstract driving machine. In some cases, the exact computation and exploitation of these sequences may be too expensive. We propose methods to compute subsets of input don't care sequences. We discuss the implementation of these algorithms using implicit methods. We also present approximate methods for managing the complexity of large FSM networks. Finally, we give some preliminary results on small networks. •This projectwas supportedby DARPA undercontractnumberJFBI90-073 and NSF undercontractnumber EMC-84-19744.
16437816
Fast Computation of Reachability Labeling for Large Graphs
The need of processing graph reachability queries stems from many applications that manage complex data as graphs. The applications include transportation network, Internet traffic analyzing, Web navigation, semantic web, chemical informatics and bio-informatics systems, and computer vision. A graph reachability query, as one of the primary tasks, is to find whether two given data objects, u and v, are related in any ways in a large and complex dataset. Formally, the query is about to find if v is reachable from u in a directed graph which is large in size. In this paper, we focus ourselves on building a reachability labeling for a large directed graph, in order to process reachability queries efficiently. Such a labeling needs to be minimized in size for the efficiency of answering the queries, and needs to be computed fast for the efficiency of constructing such a labeling. As such a labeling, 2-hop cover was proposed for arbitrary graphs with theoretical bounds on both the construction cost and the size of the resulting labeling. However, in practice, as reported, the construction cost of 2-hop cover is very high even with super power machines. In this paper, we propose a novel geometry-based algorithm which computes high-quality 2-hop cover fast. Our experimental results verify the effectiveness of our techniques over large real and synthetic graph datasets.