scholarly journals Word-Level Embeddings for Cross-Task Transfer Learning in Speech Processing

Author(s):  
Pierre Beckmann ◽  
Mikolaj Kegler ◽  
Milos Cernak
2020 ◽  
Vol 191 ◽  
pp. 105233 ◽  
Author(s):  
Xin Zheng ◽  
Luyue Lin ◽  
Bo Liu ◽  
Yanshan Xiao ◽  
Xiaoming Xiong

Author(s):  
Anton Batliner ◽  
Bernd Möbius

Automatic speech processing (ASP) is understood as covering word recognition, the processing of higher linguistic components (syntax, semantics, and pragmatics), and the processing of computational paralinguistics (CP), which deals with speaker states and traits. This chapter attempts to track the role of prosody in ASP from the word level up to CP. A short history of the field from 1980 to 2020 distinguishes the early years (until 2000)—when the prosodic contribution to the modelling of linguistic phenomena, such as accents, boundaries, syntax, semantics, and dialogue acts, was the focus—from the later years, when the focus shifted to paralinguistics; prosody ceased to be visible. Different types of predictor variables are addressed, among them high-performance power features as well as leverage features, which can also be employed in teaching and therapy.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20245-20256 ◽  
Author(s):  
Junying Gan ◽  
Li Xiang ◽  
Yikui Zhai ◽  
Chaoyun Mai ◽  
Guohui He ◽  
...  

2010 ◽  
Vol 58 (7) ◽  
pp. 866-871 ◽  
Author(s):  
Fernando Fernández ◽  
Javier García ◽  
Manuela Veloso

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuanci Zheng ◽  
Jie Li ◽  
Hongfei Ji ◽  
Lili Duan ◽  
Maozhen Li ◽  
...  

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.


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