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• W4225501245 abstract "With the success of neural network based modeling in auto-matic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined stan-dard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained su-pervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an ex-isting acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance." @default.
• W4225501245 created "2022-05-05" @default.
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• W4225501245 date "2021-12-13" @default.
• W4225501245 modified "2023-09-28" @default.
• W4225501245 title "On Architectures and Training for Raw Waveform Feature Extraction in ASR" @default.
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• W4225501245 doi "https://doi.org/10.1109/asru51503.2021.9688123" @default.
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