Spoken language recognition on a DSP array processor

1994 ◽  
Vol 5 (7) ◽  
pp. 697-703 ◽  
Author(s):  
S. Glinski ◽  
D. Roe
Keyword(s):  
Spoken Language ◽  
Array Processor ◽  
Language Recognition ◽  
Dsp Array

Spoken Language Recognition With Prosodic Features

2013 ◽  
Vol 21 (9) ◽  
pp. 1841-1853 ◽  
Author(s):  
Raymond W. M. Ng ◽  
Tan Lee ◽  
Cheung-Chi Leung ◽  
Bin Ma ◽  
Haizhou Li
Keyword(s):  
Spoken Language ◽  
Prosodic Features ◽  
Language Recognition

A study of term weighting in phonotactic approach to spoken language recognition

2010 ◽  
Author(s):  
Sirinoot Boonsuk ◽  
Donglai Zhu ◽  
Bin Ma ◽  
Atiwong Suchato ◽  
Proadpran Punyabukkana ◽  
...  
Keyword(s):  
Spoken Language ◽  
Language Recognition ◽  
Term Weighting

Dimensionality reduction of phone log-likelihood ratio features for spoken language recognition

2013 ◽  
Author(s):  
Mireia Diez ◽  
Amparo Varona ◽  
Mikel Penagarikano ◽  
Luis Javier Rodríguez-Fuentes ◽  
Germán Bordel
Keyword(s):  
Dimensionality Reduction ◽  
Likelihood Ratio ◽  
Spoken Language ◽  
Language Recognition ◽  
Log Likelihood ◽  
Log Likelihood Ratio

scholarly journals Investigation of Spoken-Language Detection and Classification in Broadcasted Audio Content

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 211 ◽  
Author(s):  
Rigas Kotsakis ◽  
Maria Matsiola ◽  
George Kalliris ◽  
Charalampos Dimoulas
Keyword(s):  
Data Augmentation ◽  
Spoken Language ◽  
Language Recognition ◽  
Adaptive Classification ◽  
Audio Recordings ◽  
Generic Language ◽  
Media Monitoring ◽  
Language Detection ◽  
Specific Radio ◽  
Language Classification

The current paper focuses on the investigation of spoken-language classification in audio broadcasting content. The approach reflects a real-word scenario, encountered in modern media/monitoring organizations, where semi-automated indexing/documentation is deployed, which could be facilitated by the proposed language detection preprocessing. Multilingual audio recordings of specific radio streams are formed into a small dataset, which is used for the adaptive classification experiments, without seeking—at this step—for a generic language recognition model. Specifically, hierarchical discrimination schemes are followed to separate voice signals before classifying the spoken languages. Supervised and unsupervised machine learning is utilized at various windowing configurations to test the validity of our hypothesis. Besides the analysis of the achieved recognition scores (partial and overall), late integration models are proposed for semi-automatically annotation of new audio recordings. Hence, data augmentation mechanisms are offered, aiming at gradually formulating a Generic Audio Language Classification Repository. This database constitutes a program-adaptive collection that, beside the self-indexing metadata mechanisms, could facilitate generic language classification models in the future, through state-of-art techniques like deep learning. This approach matches the investigatory inception of the project, which seeks for indicators that could be applied in a second step with a larger dataset and/or an already pre-trained model, with the purpose to deliver overall results.

scholarly journals Spoken language processing techniques for sign language recognition and translation

2008 ◽  
Vol 20 (2) ◽  
pp. 121-133 ◽  
Author(s):  
Philippe Dreuw ◽  
Daniel Stein ◽  
Thomas Deselaers ◽  
David Rybach ◽  
Morteza Zahedi ◽  
...  
Keyword(s):  
Sign Language ◽  
Language Processing ◽  
Spoken Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Spoken Language Processing ◽  
Processing Techniques
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