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检索条件"主题词=Query by Example"

共 117 条记录，以下是21-30 订阅

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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 ...

引用

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

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

引用

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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Privacy-preserving query-by-example Speech Search

Privacy-preserving Query-by-Example Speech Search

引用

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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USE OF ARTICULATORY BOTTLE-NECK FEATURES FOR query-BY-example SPOKEN TERM DETECTION IN LOW RESOURCE SCENARIOS

USE OF ARTICULATORY BOTTLE-NECK FEATURES FOR QUERY-BY-EXAMPL...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gautam Mantena Kishore Prahallad International Institute of Information Technology - Hyderabad India

ISBN: (纸本)9781479928941

For query-by-example spoken term detection (QbE-STD), generation of phone posteriorgrams requires labelled data which would be difficult for languages with low resources. One solution is to build models from rich resource languages and use them in the low resource scenario. However, phone classes are not language universal and alternate representation such as articulatory classes is explored. In this paper, we use articulatory information and their derivatives such as bottle-neck (BN) features (also referred to as articulatory BN features) for QbE-STD. We obtain Gaussian posteriorgrams of articulatory BN features in tandem with the acoustic parameters such as frequency domain linear prediction cepstral coefficients to perform the search. We compare the search performance of articulatory and phone BN features and show that articulatory BN features are a better representation. We also provide experimental results to show that low amounts (30 mins) of training data could be used to derive articulatory BN features.

关键词： Language Telephone Acoustic data label data Frequency-domain analysis Tandem query by example

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query-BY-example SPOKEN TERM DETECTION USING ATTENTION-BASED MULTI-HOP NETWORKS

QUERY-BY-EXAMPLE SPOKEN TERM DETECTION USING ATTENTION-BASED...

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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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COMBINING TEMPORAL AND SPECTRAL INFORMATION FOR query-BY-example SPOKEN TERM DETECTION

COMBINING TEMPORAL AND SPECTRAL INFORMATION FOR QUERY-BY-EXA...

引用

European Signal Processing Conference

作者： Ciro Gracia Xavier Anguera Xavier Binefa Universitat Pompeu Fabra Dept. of Information and Communications Technologies Telefonica Research Edificio Telefonica-Diagonal

ISBN: (纸本)9781479946037

We present a system for query-by-example Spoken Term Detection on zero-resource languages. The system compares speech patterns by representing the signal using two different acoustic models, a Spectral Acoustic (SA) model covering the spectral characteristics of the signal, and a Temporal Acoustic (TA) model covering the temporal evolution of the speech signal. Given a query and a utterance to be compared, first we compute their posterior probabilities according to each of the two models, compute similarity matrices for each model and combine these into a single enhanced matrix. Subsequence-Dynamic Time Warping (S-DTW) algorithm is used to find optimal subsequence alignment paths on this final matrix. Our experiments on data from the 2013 Spoken Web Search (SWS) task at Mediaeval benchmark evaluation show that this approach provides state of the art results and significantly improves both the single model strategies and the standard metric baselines.

关键词： query by example Zero resources languages Unsupervised learning Long temporal context

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GRAPH INDEX BASED query-BY-example SEARCH ON A LARGE SPEECH DATA SET

GRAPH INDEX BASED QUERY-BY-EXAMPLE SEARCH ON A LARGE SPEECH ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Kazuo Aoyama Atsunori Ogawa Takashi Hattori Takaaki Hori Atsushi Nakamura NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Seika-cho Soraku-gun Kyoto 619-0237 Japan

ISBN: (纸本)9781479903573

This paper presents a neighborhood graph index approach for query-by-example search using dynamic time warping (DTW) on Gaussian mixture model (GMM) posteriorgram sequences. The approach is intended to achieve a significant speed-up of a spoken term detection (STD) task for resource-limited situations. The proposed method employs a degree-reduced k-nearest neighbor (k-DR) graph as an index. A set of k-DR graphs is pre-constructed off-line from a large number of GMM posteriorgram sequences. Given a query posteriorgram sequence, one k-DR graph is selected from the set as the index. By applying a newly introduced combination of greedysearch (GS) and breadth-first search (BFS) algorithms to the selected k-DR graph index, the proposed method efficiently achieves query-by-example STD. Experimental results on the MIT lecture corpus demonstrate that the proposed method works much faster than a state-of-art method by more than one order magnitude, keeping almost the same precision.

关键词： Neighborhood graph index Spoken term detection query-by-example search Dynamic time warping Gaussian mixture model posteriorgram dynamic time warping query by example Line graph Speech Gm(m) LARGE gene Sexually Transmitted Diseases SET

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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...

引用

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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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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query-by-example keyword spotting using long short-term memory networks

Query-by-example keyword spotting using long short-term memo...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： G. Chen C. Parada T. N. Sainath Center for Language & Speech Process. Johns Hopkins Univ. Baltimore MD USA

ISBN: (纸本)9781467369985

We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model. We use the activations prior to the softmax layer of the LSTM as our keyword-vector. At runtime, we detect the keyword by extracting the same feature vector from a sliding window and computing a simple similarity score between this test vector and the keyword vector. With clean speech, we achieve 86% relative false rejection rate reduction at 0.5% false alarm rate when compared to a competitive phoneme posteriorgram with dynamic time warping KWS system, while the reduction in the presence of babble noise is 67%. Our system has a small memory footprint, low computational cost, and high precision, making it suitable for on-device applications.

关键词： Eigenvector query by example test vectors Short-Term Memory Juvenile Spinal Muscular Atrophy Metrorrhagia KEYWORD sliding windows Runtime

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