Development of Speech Recognition Systems in Emergency Call Centers
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 contex...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/4/634 |
| _version_ | 1797538228334493696 |
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| author | Alakbar Valizada Natavan Akhundova Samir Rustamov |
| author_facet | Alakbar Valizada Natavan Akhundova Samir Rustamov |
| author_sort | Alakbar Valizada |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:27:33Z |
| format | Article |
| id | doaj.art-f15b60972a944b4f9ab6ab2aaa422369 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T12:27:33Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-f15b60972a944b4f9ab6ab2aaa4223692023-11-21T14:56:17ZengMDPI AGSymmetry2073-89942021-04-0113463410.3390/sym13040634Development of Speech Recognition Systems in Emergency Call CentersAlakbar Valizada0Natavan Akhundova1Samir Rustamov2Artificial Intelligence Laboratory, ATL Tech, Jalil Mammadguluzadeh 102A, Baku 1022, AzerbaijanArtificial Intelligence Laboratory, ATL Tech, Jalil Mammadguluzadeh 102A, Baku 1022, AzerbaijanSchool of Information Technologies and Engineering, ADA University, Ahmadbey Aghaoglu Str. 11, Baku 1008, AzerbaijanIn 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.https://www.mdpi.com/2073-8994/13/4/634speech recognitionGMMHMMDNNKaldicall center |
| spellingShingle | Alakbar Valizada Natavan Akhundova Samir Rustamov Development of Speech Recognition Systems in Emergency Call Centers Symmetry speech recognition GMM HMM DNN Kaldi call center |
| title | Development of Speech Recognition Systems in Emergency Call Centers |
| title_full | Development of Speech Recognition Systems in Emergency Call Centers |
| title_fullStr | Development of Speech Recognition Systems in Emergency Call Centers |
| title_full_unstemmed | Development of Speech Recognition Systems in Emergency Call Centers |
| title_short | Development of Speech Recognition Systems in Emergency Call Centers |
| title_sort | development of speech recognition systems in emergency call centers |
| topic | speech recognition GMM HMM DNN Kaldi call center |
| url | https://www.mdpi.com/2073-8994/13/4/634 |
| work_keys_str_mv | AT alakbarvalizada developmentofspeechrecognitionsystemsinemergencycallcenters AT natavanakhundova developmentofspeechrecognitionsystemsinemergencycallcenters AT samirrustamov developmentofspeechrecognitionsystemsinemergencycallcenters |