Towards mixed language speech recognition systems

Exploring E2E speech recognition systems for new languages

10.21437/iberspeech.2018-22 ◽

2018 ◽

Cited By ~ 1

Author(s):

Conrad Bernath ◽

Aitor Alvarez ◽

Haritz Arzelus ◽

Carlos David Martínez

Keyword(s):

Speech Recognition ◽

Recognition Systems

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

10.21437/interspeech.2019-2112 ◽

2019 ◽

Cited By ~ 2

Author(s):

Sheng Li ◽

Dabre Raj ◽

Xugang Lu ◽

Peng Shen ◽

Tatsuya Kawahara ◽

...

Keyword(s):

Speech Recognition ◽

Recognition Systems

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SpecMark: A Spectral Watermarking Framework for IP Protection of Speech Recognition Systems

10.21437/interspeech.2020-2787 ◽

2020 ◽

Author(s):

Huili Chen ◽

Bita Darvish ◽

Farinaz Koushanfar

Keyword(s):

Speech Recognition ◽

Ip Protection ◽

Recognition Systems

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

Procedia CIRP ◽

10.1016/j.procir.2020.05.214 ◽

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

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Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽

10.3390/sym13040634 ◽

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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