Results 181 to 190 of about 1,466 (245)

The analysis of transformer end-to-end model in Real-time interactive scene based on speech recognition technology. [PDF]

Sci Rep
Li P, Yang C, Mao L.
europepmc   +1 more source

Query-by-example Spoken Term Detection For OOV terms

2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009
The 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, Abhinav Sethy, Bhuvana Ramabhadran   +2 more
semanticscholar   +3 more sources

Query-by-Example Spoken Term Detection using Attentive Pooling Networks

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2019
Query-by-example spoken term detection (QbE-STD) is attractive because its a key technology for retrieving and browsing spoken content without transcribing them into text. Several end-to-end models based on encoder architecture have been proposed for QbE-STD, in which the input pair, spoken query and audio segment, are first projected into fixed-length
Kun Zhang, Zhiyong Wu, Jia Jia, Helen Meng, Binheng Song   +4 more
semanticscholar   +3 more sources

Query-by-Example Spoken Term Detection for Zero-Resource Languages Using Heuristic Search

ACM Transactions on Asian and Low-Resource Language Information Processing, 2023
Query-by-Example spoken content retrieval is a demanding and challenging task when a large volume of spoken content is piled up in the repositories without annotation. In the absence of annotation, spoken content retrieval is achieved by capturing the similarities between the query and spoken terms from the acoustic feature representation itself ...
P. Sudhakar, Sreenivasa Rao K, Pabitra Mitra   +2 more
semanticscholar   +3 more sources

Multilingual query-by-example spoken term detection in Indian languages

International Journal of Speech Technology, 2019
Spoken language processing poses to be a challenging task in multilingual and mixlingual scenario in linguistically diverse regions like Indian subcontinent. Common articulatory based framework is explored for the representation of phonemes of different languages.
Abhimanyu Popli, Arun Kumar
semanticscholar   +3 more sources

An acoustic segment modeling approach to query-by-example spoken term detection

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
The framework of posteriorgram-based template matching has been shown to be successful for query-by-example spoken term detection (STD). This framework employs a tokenizer to convert query examples and test utterances into frame-level posteriorgrams, and applies dynamic time warping to match the query posteriorgrams with test posteriorgrams to locate ...
Haipeng Wang, Cheung-Chi Leung, Tan Lee, Bin Ma, Haizhou Li   +4 more
semanticscholar   +3 more sources

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Query-by-example spoken term detection using phonetic posteriorgram templates

2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009
This paper examines a query-by-example approach to spoken term detection in audio files. The approach is designed for low-resource situations in which limited or no in-domain training material is available and accurate word-based speech recognition capability is unavailable.
Timothy J. Hazen, Wade Shen, Christopher White   +2 more
openaire   +2 more sources

Unsupervised query-by-example spoken term detection based on DPHMM tokenizer

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017
This paper investigates the use of Dirichlet process hidden Markov model (DPHMM) tokenizer for the template matching based query-by-example spoken term detection (QbE-STD) task. DPHMM can be obtained following an unsupervised iterative procedure without any training transcriptions. The STD performance of the DPHMM tokenizer is evaluated on TIMIT Corpus.
Cao Jiankai, Lianhai Zhang
openalex   +2 more sources

Multitask Feature Learning for Low-Resource Query-by-Example Spoken Term Detection

IEEE Journal of Selected Topics in Signal Processing, 2017
We propose a novel technique that learns a low-dimensional feature representation from unlabeled data of a target language, and labeled data from a nontarget language. The technique is studied as a solution to query-by-example spoken term detection (QbE-STD) for a low-resource language.
Hongjie Chen, Cheung-Chi Leung, Lei Xie, Bin Ma, Haizhou Li   +4 more
openaire   +2 more sources