Results 11 to 20 of about 1,466 (245)

H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing [PDF]

Interspeech 2025
Query-by-example spoken term detection (QbE-STD) searches for matching words or phrases in an audio dataset using a sample spoken query. When annotated data is limited or unavailable, QbE-STD is often done using template matching methods like dynamic time warping (DTW), which are computationally expensive and do not scale well.
Singh, Akanksha, Chen, Yi-Ping Phoebe, Arora, Vipul   +2 more
semanticscholar   +4 more sources

Exploring the Effectiveness of Feature Reduction and Kernel-Based Matching for Query-by- Example Spoken Term Detection Using CNN

IEEE Access
Query-by-example spoken term detection (QbE-STD) refers to the search for an audio query in a repository of audio utterances. A common approach for QbE-STD involves computing a matching matrix between the feature representations of the query and the ...
Manisha Naik Gaonkar, Veena Thenkanidiyoor, Dileep Aroor Dinesh, H. Muralikrishna   +3 more
doaj   +3 more sources

High-performance Query-by-Example Spoken Term Detection on the SWS 2013 evaluation

2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2014
Language independent query-by-example spoken term detection (QbE-STD) is the problem of retrieving audio documents from an archive, which contain a spoken query provided by a user. This is usually casted as a hypothesis testing and pattern matching problem, also referred to as a ``zero-resource task'' since no specific training or lexical information ...
Luis Javier Rodríguez-Fuentes, Amparo Varona, Mikel Peñagarikano, Germán Bordel, Mireia Díez   +4 more
semanticscholar   +5 more sources

CNN Based Query by Example Spoken Term Detection [PDF]

Interspeech 2018, 2018
In this work, we address the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State of the art solutions usually rely on dynamic time warping (DTW) based template matching.
Dhananjay Ram, Lesly Miculicich, Hervé Bourlard   +2 more
semanticscholar   +4 more sources

Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection [PDF]

2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2020
In this paper, we propose a deep convolutional neural network-based acoustic word embedding system on code-switching query by example spoken term detection. Different from previous configurations, we combine audio data in two languages for training instead of only using one single language.
Murong Ma, Haiwei Wu, Xuyang Wang, Lin Yang, Junjie Wang, Ming Li   +5 more
semanticscholar   +6 more sources

Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations [PDF]

EURASIP Journal on Audio, Speech, and Music Processing, 2016
Xunta de Galicia | Ref.
Javier Tejedor, Doroteo T. Toledano, Paula Lopez‐Otero, Laura Docío-Fernández, Carmén García Mateo   +4 more
semanticscholar   +6 more sources

Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach [PDF]

arXiv.org
Query-by-example spoken term detection (QbE-STD) is typically constrained by transcribed data scarcity and language specificity. This paper introduces a novel, language-agnostic QbE-STD model leveraging image processing techniques and transformer architecture.
Allahdadi Fatemeh, Mahdian Toroghi Rahil, Hassan Zareian   +2 more
semanticscholar   +4 more sources

Query-by-Example Spoken Term Detection: A Systematic Review [PDF]

International Journal of Electronics and Communication Engineering
Manisha Naik Gaonkar, Veena Thenakanidiyoor, A. D. Dileep   +2 more
semanticscholar   +3 more sources

Phonological Posterior Hashing for Query by Example Spoken Term Detection [PDF]

Interspeech 2018, 2018
State of the art query by example spoken term detection (QbE-STD) systems in zero-resource conditions rely on representation of speech in terms of sequences of class-conditional posterior probabilities estimated by deep neural network (DNN).
Afsaneh Asaei, Dhananjay Ram, Hervé Bourlard   +2 more
semanticscholar   +4 more sources

Self-Supervised Spoken Term Detection for Query by Example

Ingénierie des systèmes d information
Nadia Benati, Halima Bahi
semanticscholar   +3 more sources