Results 211 to 220 of about 1,466 (245)

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

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2015
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.
Haihua Xu, Peng Yang, Xiong Xiao, Lei Xie, C. Leung, Hongjie Chen, J. Yu, Hang Lv, Lei Wang, S. Leow, B. Ma, Chng Eng Siong, Haizhou Li   +12 more
semanticscholar   +1 more source

Combined MFCC-FBCC Features for Unsupervised Query-by-Example Spoken Term Detection

2015
A new set of features for addressing the problem of unsupervised spoken term detection is proposed in this paper. If we have a large audio database, the objective of this system is to find a spoken query in the databases. In unsupervised audio search, language specific resources are not required.
Drisya Vasudev, Suryakanth V. Vasudev, K. K. Anish Babu, K. S. Riyas   +3 more
openaire   +1 more source

Query-by-example spoken term detection using bottleneck feature and Hidden Markov model

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015
Query by example spoken term detection (QbE-STD) is an effective search mechanism to find spoken queries in spoken audio, especially for the source limited language. The dynamic time warping (DTW) algorithm is the state-of-art algorithm in this area. This paper presents some methods to improve the QbE-STD performance.
null Xue Liu, null Wu Guo, null Niansong Wang   +2 more
openaire   +1 more source

A Refined Query-by-Example Approach to Spoken-Term-Detection on ESL learners’ Speech

2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2018
A refined Query-by-Example (QbE) approach is proposed to improve Spoken-Term-Detection (STD) performance on L2 English learners’ speech data. A Hidden Markov Model (HMM) is built for each keyword and a computationally efficient, iterative Viterbi decoding is adopted to detect spoken keywords in test.
Jingyong Hou, Wenping Hu, Frank K. Soong, Lei Xie   +3 more
openaire   +1 more source

Cancer Statistics, 2021

Ca-A Cancer Journal for Clinicians, 2021
Rebecca L Siegel, Kimberly D Miller
exaly  

StyU-STD: Style-Diverse Sample Generation from Unlabeled Data for Query-by-Example Spoken Term Detection

IEEE International Conference on Systems, Man and Cybernetics
Hanyu Ding, Lijian Gao, Wenlong Dong, Xiangrui Li, Qirong Mao   +4 more
semanticscholar   +1 more source

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

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017
Yougen Yuan, C. Leung, Lei Xie, Hongjie Chen, B. Ma, Haizhou Li   +5 more
semanticscholar   +1 more source

Exploring the Impact of Different Similarity Measures on Query-by-Example Spoken Term Detection

SN Computer Science
M. Gaonkar, Veena Thenkanidiyoor, A. D. Dileep   +2 more
semanticscholar   +1 more source

Cancer statistics, 2020

Ca-A Cancer Journal for Clinicians, 2020
Rebecca L Siegel, Kimberly D Miller
exaly  

Soft‐tissue sarcoma in adults: An update on the current state of histiotype‐specific management in an era of personalized medicine

Ca-A Cancer Journal for Clinicians, 2020
Adriana C Gamboa, Alessandro Gronchi, Kenneth Cardona, Facs   +2 more
exaly