Keynote speech 1: An integrated deep learning approach to acoustic signal pre-processing and acoustic modeling with applications to robust automatic speech recognition

2017 ◽  
Author(s):  
Chin-Hui Lee
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Acoustic Signal ◽  
Acoustic Modeling ◽  
Learning Approach ◽  
Keynote Speech

scholarly journals Evaluation of Automatic Speech Recognition Systems

2021 ◽  
Author(s):  
Matheus Xavier Sampaio ◽  
Regis Pires Magalhães ◽  
Ticiana Linhares Coelho da Silva ◽  
Lívia Almada Cruz ◽  
Davi Romero de Vasconcelos ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Smart Homes ◽  
The Other ◽  
Learning Models ◽  
Recognition Systems ◽  
Microsoft Azure

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.

scholarly journals Selection of acoustic modeling unit for Tibetan speech recognition based on deep learning

2021 ◽  
Vol 336 ◽  
pp. 06014
Author(s):  
Baojia Gong ◽  
Rangzhuoma Cai ◽  
Zhijie Cai ◽  
Yuntao Ding ◽  
Maozhaxi Peng
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Experimental Verification ◽  
Character Segmentation ◽  
Acoustic Modeling ◽  
Overall Performance ◽  
Primary Problem ◽  
Selection Of

The selection of the speech recognition modeling unit is the primary problem of acoustic modeling in speech recognition, and different acoustic modeling units will directly affect the overall performance of speech recognition. This paper designs the Tibetan character segmentation and labeling model and algorithm flow for the purpose of solving the problem of selecting the acoustic modeling unit in Tibetan speech recognition by studying and analyzing the deficiencies of the existing acoustic modeling units in Tibetan speech recognition. After experimental verification, the Tibetan character segmentation and labeling model and algorithm achieved good performance of character segmentation and labeling, and the accuracy of Tibetan character segmentation and labeling reached 99.98%, respectively.

Acoustic signal-based deep learning approach for smart sorting of pistachio nuts

2022 ◽  
Vol 185 ◽  
pp. 111778
Author(s):  
Mohammad Hosseinpour-Zarnaq ◽  
Mahmoud Omid ◽  
Amin Taheri-Garavand ◽  
Amin Nasiri ◽  
Asghar Mahmoudi
Keyword(s):  
Deep Learning ◽  
Acoustic Signal ◽  
Learning Approach ◽  
Pistachio Nuts
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