A Vulnerability Test Method for Speech Recognition Systems Based on Frequency Signal Processing

2018 ◽  
Author(s):  
Honghao Yang ◽  
Dong Liang ◽  
Xiaohui Kuang ◽  
Changqiao Xu
Keyword(s):  
Signal Processing ◽  
Speech Recognition ◽  
Test Method ◽  
Frequency Signal ◽  
Recognition Systems

scholarly journals AUTOMATIC IDENTIFICATION OF VIETNAMESE DIALECTS

2016 ◽  
Vol 32 (1) ◽  
pp. 19-30
Author(s):  
Pham Ngoc Hung ◽  
Trinh Van Loan ◽  
Nguyen Hong Quang
Keyword(s):  
Signal Processing ◽  
Speech Recognition ◽  
Fundamental Frequency ◽  
Automatic Identification ◽  
Recognition Process ◽  
The World ◽  
Recognition Systems ◽  
Frequency Features

The dialect identification was studied for many languages over the world nevertheless the research on signal processing for Vietnamese dialects is still limited and there were not many published works. There are many different dialects for Vietnamese. The influence of dialectal features on speech recognition systems is important. If the information about dialects is known during speech recognition process, the performance of recognition systems will be better because the corpus of these systems is normally organized according to different dialects. This paper will present the combination of MFCC coefficients and fundamental frequency features of Vietnamese for dialectal identification based on GMM. The experiment result for the dialect corpus of Vietnamese shows that the performance of dialectal identification is increased from 59% for the case using only MFCC coefficients to 71% for the case using MFCC coefficients and the information of fundamental frequency.

Exploring E2E speech recognition systems for new languages

2018 ◽  
Author(s):  
Conrad Bernath ◽  
Aitor Alvarez ◽  
Haritz Arzelus ◽  
Carlos David Martínez
Keyword(s):  
Speech Recognition ◽  
Recognition Systems

Improving Transformer-Based Speech Recognition Systems with Compressed Structure and Speech Attributes Augmentation

2019 ◽  
Author(s):  
Sheng Li ◽  
Dabre Raj ◽  
Xugang Lu ◽  
Peng Shen ◽  
Tatsuya Kawahara ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Recognition Systems

scholarly journals Human-robot-interaction using cloud-based speech recognition systems

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 130-135
Author(s):  
Christian Deuerlein ◽  
Moritz Langer ◽  
Julian Seßner ◽  
Peter Heß ◽  
Jörg Franke
Keyword(s):  
Speech Recognition ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Recognition Systems

scholarly journals Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 634
Author(s):  
Alakbar Valizada ◽  
Natavan Akhundova ◽  
Samir Rustamov
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov ◽  
Call Centers ◽  
The Other ◽  
Language Models ◽  
Emergency Call ◽  
Acoustic Model ◽  
Different Types ◽  
Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

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