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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chia-Wei Ao Hung-yi Lee Graduate Institute of Communication Engineering National Taiwan University

ISBN: (纸本)9781538646595

Retrieving spoken content with spoken queries, or query-by-example spoken term detection (STD), is attractive because it makes possible the matching of signals directly on the acoustic level without transcribing them into text. Here, we propose an end-to-end query-by-example STD model based on an attention-based multi-hop network, whose input is a spoken query and an audio segment containing several utterances;the output states whether the audio segment includes the query. The model can be trained in either a supervised scenario using labeled data, or in an unsupervised fashion. In the supervised scenario, we find that the attention mechanism and multiple hops improve performance, and that the attention weights indicate the time span of the detected terms. In the unsupervised setting, the model mimics the behavior of DTW, and it performs as well as DTW but with a lower run-time complexity.

关键词： Attention-based Multi-hop Network query by example Transcribing Sexually Transmitted Diseases Audio sound level wireless multi-hop networks

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Pairwise learning using multi-lingual bottleneck features for low-resource query-by-example spoken term detection

Pairwise learning using multi-lingual bottleneck features fo...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yougen Yuan Cheung-Chi Leung Lei Xie Hongjie Chen Bin Ma Haizhou Li School of Computer Science Northwestern Polytechnical University Xi'an China Institute for Infocomm Research A*STAR Singapore

ISBN: (纸本)9781509041183

We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages. The multi-lingual BNFs of the word pairs are used as an initial feature representation to train an autoencoder (AE). We extract features from an internal hidden layer of the pairwise trained AE to perform acoustic pattern matching for QbE-STD. Our experiments on the TIMIT and Switchboard corpora show that the pairwise learning brings 7.61% and 8.75% relative improvements in mean average precision (MAP) respectively over the initial feature representation.

关键词： pairwise learning bottleneck features autoencoder spoken term detection low-resource speech processing query by example Pairwise Traffic Bottlenecks Word Pattern matching

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EFFECTIVE UTILIZATION OF MULTIPLE exampleS IN query-BY-example SPOKEN TERM DETECTION

EFFECTIVE UTILIZATION OF MULTIPLE EXAMPLES IN QUERY-BY-EXAMP...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ji Xu Ge Zhang Yonghong Yan The Key Laboratory of Speech Acoustics and Content Understanding Chinese Academy of Sciences Beijing 100190 P.R.China

ISBN: (纸本)9781479999897

This paper investigates the example utilization problem in query-by-example spoken term detection when multiple examples are provided for each query term. To achieve this goal, we propose three evaluation metrics to assess the quality of all the examples, namely posteriorgram stability score, pronunciation reliability score and local similarity score. We also present a clustering based example generation approach to creating better examples based on the original ones. Experiments conducted on a telephone speech corpus shows that it is better to use several representative examples selected by the quality assessment process than to simply use all the examples. Furthermore, even better results can be obtained if the generated examples are used.

关键词： spoken term detection query-byexample multiple examples utilization example quality assessment example generation effective utilization query by example Process Assessment (Health Care) Pronunciation indium Quality assessment

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Real-time selection of video streams for live TV broadcasting based on query-by-example using a 3D model

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第8期74卷 2659-2685页

作者： Yus, Roberto Ilarri, Sergio Mena, Eduardo Univ Zaragoza IIS Dept Zaragoza 50018 Spain

The emergence of low-cost cameras with nearly professional features in the consumer market represents a new important source of video information. For example, using an increasing number of these cameras in live TV broadcastings enables obtaining varied contents without affecting the production costs. However, searching for interesting shots (e.g., a certain view of a specific car in a race) among many video sources in real-time can be difficult for a Technical Director (TD). So, TDs require a mechanism to easily and precisely represent the kind of shot they want to obtain abstracting them from the need to be aware of all the views provided by the cameras. In this paper we present our proposal to help a TD to visually define, using an interface for the definition of 3D scenes, an interesting sample view of one or more objects in the scenario. We recreate the views of the cameras in a 3D engine and apply 3D geometric computations on their virtual view, instead of analyzing the real images they provide, to enable an efficient and precise real-time selection. Specifically, our system computes a similarity measure to rank the candidate cameras. Moreover, we present a prototype of the system and an experimental evaluation that shows the interest of our proposal.

关键词： query by example Camera shot similarity computation User interfaces to manage 3D scenes

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query-by-example Spoken Term Detection Using Low Dimensional Posteriorgrams Motivated by Articulatory Classes 17

Query-by-Example Spoken Term Detection Using Low Dimensional...

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IEEE 17th International Workshop on Multimedia Signal Processing (MMSP 2015)

作者： Popli, Abhimanyu Kumar, Arun Indian Inst Technol Delhi Ctr Appl Res Elect New Delhi 110016 India Ctr Dev Telemat New Delhi 110030 India

ISBN: (纸本)9781467374781

This paper addresses the problem of query-by-example Spoken Term Detection (QbE-STD). Posteriorgrams have been widely used in the research on QbE-STD. Features based on articulatory classes are known to be robust to phonemic variations. The articulatory features like voicing and place of articulation are the main distinguishing features among some plosives and fricatives. These properties inspire the study of posteriorgrams based on articulatory classes for QbE-STD. Most of the previous works based on articulatory features have defined a large number of articulatory classes making it difficult to use them directly for pattern matching. Also, most of the works have completely ignored the uniqueness of the phonemes having transitory places of articulation eg. diphthongs and approximants. These issues have been addressed in this work while carefully selecting low dimensional articulatory motivated (LDAM) posteriorgrams on the basis of detailed experiments. This work is the first to show that the articulatory based posteriorgrams can outperform the phonemic posteriorgram significantly in a stand alone way (without any support from acoustic or phonemic features) for the task of QbE-STD.

关键词： query by example Joints Voice Phonemes DIMENSIONAL Acoustics uniqueness

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querying knowledge graphs through positive and negative examples and feedback

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第5期62卷 1165-1186页

作者： Akritidis, Akritas Tzitzikas, Yannis Fdn Res & Technol Hellas FORTH Inst Comp Sci N Plastira 100 Iraklion Greece Univ Crete Dept Comp Sci Iraklion Crete Greece

The formulation of structured queries over Knowledge Graphs is not an easy task. To alleviate this problem, we propose a novel interactive method for SPARQL query formulation, for enabling users (plain and advanced) to formulate gradually queries by providing examples and various kinds of positive and negative feedback, in a manner that does not pre-suppose knowledge of the query language or the contents of the Knowledge Graph. In comparison to other example-based query approaches, distinctive features of our approach is the support of negative examples, and the positive/negative feedback on the generated constraints. We detail the algorithmic aspect and we present an interactive user interface that implements the approach. The application of the model on real datasets from DBpedia (Movies, Actors) and other datasets (scientific papers), showcases the feasibility and the effectiveness of the approach. A task-based evaluation that included users that are not familiar with SPARQL, provided positive evidence that the interaction is easy-to-grasp and enabled most users to formulate the desired queries.

关键词： query by example Knowledge graphs

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A Review of Deep Learning Techniques in Document Image Word Spotting

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ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022年第2期29卷 1085-1106页

作者： Kumari, Lalita Sharma, Anuj Panjab Univ Dept Comp Sci & Applict Chandigarh India

From the early days of pattern recognition, word spotting have been important test beds for studying how well machines can perform better decision making. In recent years, word spotting have made dramatic advances with state-of-the-art techniques reaching high level of performance in real life applications. This word spotting domain have driven research by providing suitable yet well-defined challenges for pattern recognition and document analysis practitioners. We continue in this direction by covering extensive literature and new challenges in this domain with comparison of previous work. In particular, we have covered recent deep learning technique role in word spotting and future scope of word spotting with deep learning. We believe writing suitable review of word spotting will not only be crucial for understanding of this field in today era, but also in broader collaborative efforts, especially those with artificial intelligence based tasks. To facilitate future research in word spotting, we have discussed word spotting from learning environment, including its framework design with components as query phase, preprocessing stages, segmentation, feature extraction, feature representation and matching process strategies. Further, deep learning working and use in word spotting architecture has been discussed. The study also include an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.

关键词： Document image Word spotting Segmentation Documents indexing Convolutional neural network Deep learning Feature representation query by string query by example

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Language independent query-by-example spoken term detection using N-best phone sequences and partial matching

Language independent query-by-example spoken term detection ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： H. Xu P. Yang X. Xiao L. Xie C. C. Leung Hongjie Chen Jia Yu Hang Lv L. Wang S. J. Leow B. Ma E. S. Chng H. Li Temasek Lab. Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467369985

In this paper, we propose a partial sequence matching based symbolic search (SS) method for the task of language independent query-by-example spoken term detection. One main drawback of conventional SS approach is the high miss rate for long queries. This is due to high variations in symbol representation of query and search audios, especially in language independent scenario. The successful matching of a query with its instances in search audio becomes exponentially more difficult as the query grows longer. To reduce miss rate, we propose a partial matching strategy, in which all partial phone sequences of a query are used to search for query instances. The partial matching is also suitable for real life applications where exact match is usually not necessary and word prefix, suffix, and order should not affect the search result. When applied to the QUESST 2014 task, results show the partial matching of phone sequences is able to reduce miss rate of long queries significantly compared with conventional full matching method. In addition, for the most challenging inexact matching queries (type 3), it also shows clear advantage over DTW-based methods.

关键词： keyword search partial matching phone tokenizer queryby-example spoken term detection miss rate Key Search query by example Telephone Language

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Privacy-preserving query-by-example Speech Search

Privacy-preserving Query-by-Example Speech Search

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： J. Portelo A. Abad B. Raj I. Trancoso Inst. Super. Tecnico Univ. de Lisboa Lisbon Portugal

ISBN: (纸本)9781467369985

This paper investigates a new privacy-preserving paradigm for the task of query-by-example Speech Search using Secure Binary Embeddings, a hashing method that converts vector data to bit strings through a combination of random projections followed by banded quantization. The proposed method allows performing spoken query search in an encrypted domain, by analyzing ciphered information computed from the original recordings. Unlike other hashing techniques, the embeddings allow the computation of the distance between vectors that are close enough, but are not perfect matches. This paper shows how these hashes can be combined with Dynamic Time Warping based on posterior derived features to perform secure speech search. Experiments performed on a sub-set of the Speech-Dat Portuguese corpus showed that the proposed privacy-preserving system obtains similar results to its non-private counterpart.

关键词： Data Privacy Dynamic Time Warping query-by-example Speech Search Secure Binary Embeddings dynamic time warping query by example Data privacy hashing vector data Speech Cosmic strings

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Double-layer neighborhood graph based similarity search for fast query-by-example spoken term detection

Double-layer neighborhood graph based similarity search for ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： K. Aoyama A. Ogawa T. Hattori T. Hori NTT Commun. Sci. Labs. NTT Corp. Kyoto Japan

ISBN: (纸本)9781467369985

This paper presents a novel double-layer neighborhood graph index for acceleration of similarity search that accomplishes fast querybyexample spoken term detection (STD). When a query segment is given, our proposed STD method finds similar segments to the query from an utterance data set by efficient similarity search that traverses the double-layer neighborhood graph (DLG) with a low computational cost. The segment is a sequence of Gaussian mixture model posteriorgram frames and corresponds to a vertex in the DLG. A dissimilarity between vertices is measured by dynamic time warping. The DLG consists of two distinct degree-reduced k-nearest neighbor graphs in a base and an upper layer. The base layer's graph has all the vertices in the data set while the upper layer's graph includes only representatives extracted from the vertices in the base layer. By way of analogy, search in the DLG resembles driving on general roads and express highways appropriately for travel-time saving. Experimental results on the MIT lecture corpus demonstrate that the proposed method achieves CPU time reduction by 40% and more than 60% compared to the most recent method and the ordinary graphbased method, keeping almost the same precision.

关键词： Dynamic time warping Neighborhood graph query-by-example search Search index Spoken term detection dynamic time warping digital line graphs Line graph Double layer vertex head query by example BASE COURSE Neighborhood

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