Results 151 to 160 of about 288,136 (202)
Some of the next articles are maybe not open access.
H-QuEST: Accelerating Query-by-Example Spoken Term Detection with Hierarchical Indexing
InterspeechQuery-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 ...
Akanksha Singh, Y. Chen, Vipul Arora
semanticscholar +1 more source
Joint Multimodal Contrastive Learning for Robust Spoken Term Detection and Keyword Spotting
arXiv.orgAcoustic Word Embeddings (AWEs) improve the efficiency of speech retrieval tasks such as Spoken Term Detection (STD) and Keyword Spotting (KWS). However, existing approaches suffer from limitations, including unimodal supervision, disjoint optimization ...
Ramesh Gundluru +2 more
semanticscholar +1 more source
Query-by-example Spoken Term Detection For OOV terms
2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009The goal of Spoken Term Detection (STD) technology is to allow open vocabulary search over large collections of speech content. In this paper, we address cases where search term(s) of interest (queries) are acoustic examples. This is provided either by identifying a region of interest in a speech stream or by speaking the query term.
Carolina Parada +2 more
openaire +1 more source
An efficient TF-IDF based Query by Example Spoken Term Detection
Conference on Algebraic InformaticsIn the present research, we tackle the problem of query by example spoken term detection (QbE-STD) in the zero-resource scenario. State-of-the-art methods typically use dynamic temporal warping (DTW) to match templates.
Akanksha Singh +2 more
semanticscholar +1 more source
Predicting search term reliability for spoken term detection systems
International Journal of Speech Technology, 2013Spoken term detection is an extension of text-based searching that allows users to type keywords and search audio files containing recordings of spoken language. Performance is dependent on many external factors such as the acoustic channel, language, pronunciation variations and acoustic confusability of the search term.
Amir Hossein Harati Nejad Torbati +1 more
openaire +1 more source
Web derived pronunciations for spoken term detection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009Indexing and retrieval of speech content in various forms such as broadcast news, customer care data and on-line media has gained a lot of interest for a wide range of applications, from customer analytics to on-line media search. For most retrieval applications, the speech content is typically first converted to a lexical or phonetic representation ...
Dogan Can +10 more
openaire +1 more source
BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term Detection
IEEE International Conference on Acoustics, Speech, and Signal ProcessingQuery-by-example spoken term detection (QbE-STD) is often hindered by reliance on frame-level features and the computationally intensive DTW-based template matching, limiting its practicality. To address these challenges, we propose a novel approach that
Anup Singh, Kris Demuynck, Vipul Arora
semanticscholar +1 more source
Fast Lattice-Free Keyword Filtering for Accelerated Spoken Term Detection
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020We present a novel set of keyword detection techniques to accelerate spoken term detection for known queries with minimal loss in accuracy. Using only ASR frame-level acoustic posteriors we can train multiple models to effectively detect non-target ...
Jonathan Wintrode, Jenny Wilkes
semanticscholar +1 more source
Efficient spoken term detection using confusion networks
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014In this paper, we present a fast, vocabulary independent algorithm for spoken term detection (STD) that demonstrates a word-based index is sufficient to achieve good performance for both in-vocabulary (IV) and out-of-vocabulary (OOV) terms. Previous approaches have required that a separate index be built at the sub-word level and then expanded to allow
Lidia Mangu +4 more
openaire +1 more source
Cross-Lingual Query-by-Example Spoken Term Detection: A Transformer-Based Approach
arXiv.orgQuery-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 ...
Allahdadi Fatemeh +2 more
semanticscholar +1 more source