Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing
Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface fo...
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| Vydáno v: | Advanced materials (Weinheim) Ročník 32; číslo 35; s. e1904020 - n/a |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Germany
Wiley Subscription Services, Inc
01.09.2020
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| Témata: | |
| ISSN: | 0935-9648, 1521-4095, 1521-4095 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self‐powered sensors, machine learning algorithms, and speaker recognition.
Flexible piezoelectric acoustic sensors and machine learning for speech processing can change the paradigm of voice technologies for the hyperconnected society, offering personalized intelligent services such as biometric authentication, AI secretaries, and Internet‐of‐Things (IoT) appliances. The recent advances in the fields of self‐powered flexible acoustic sensors and machine learning algorithms for speech recognition are comprehensively summarized. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ISSN: | 0935-9648 1521-4095 1521-4095 |
| DOI: | 10.1002/adma.201904020 |