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- W4220695744 abstract "Artificial intelligence-based speech recognition systems are already available and capable of recognizing the French language. Still, it is quite time-consuming to compare which one will be effective for an assistant robot. The study aims to select the best French-language speech recognition system with the least error in a real environment. In this paper, we present related works on how an Automatic Speech Recognition (ASR) system works, the models used by each of its components, several open-source French datasets, and the frequently used evaluation techniques. Next, we compare deep learning-based speech recognition APIs and pre-trained models for French on two different datasets using the Word Error Rate (WER) metric. The experimental results reveal that Google's Speech-to-Text API outperforms the other systems, namely VOSK API, Wav2vec 2.0, QuartzNet, and Speech Brain's Convolutional, Recurrent, and Fully-connected Networks (CRDNN) model." @default.
- W4220695744 created "2022-04-03" @default.
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- W4220695744 date "2022-03-03" @default.
- W4220695744 modified "2023-10-01" @default.
- W4220695744 title "Which French speech recognition system for assistant robots?" @default.
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- W4220695744 doi "https://doi.org/10.1109/iraset52964.2022.9737976" @default.
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