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Memory and linguistic demands of the Token Test (Pham et al., 2022) ...
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Memory and linguistic demands of the Token Test (Pham et al., 2022) ...
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Multi language Email Classification Using Transfer learning
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End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system ...
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Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
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SHAS: Approaching optimal Segmentation for End-to-End Speech Translation ...
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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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VivesDebate: A New Annotated Multilingual Corpus of Argumentation in a Debate Tournament ...
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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The First Gospel, the Gospel of the Poor: A New Reconstruction of Q and Resolution of the Synoptic Problem based on Marcion's Early Luke ...
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation ...
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Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition ...
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Abstract:
Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility through slow, uncoordinated control of speech production muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers communicate more effectively. To have robust dysarthria-specific ASR, sufficient training speech is required, which is not readily available. Recent advances in Text-To-Speech (TTS) synthesis multi-speaker end-to-end TTS systems suggest the possibility of using synthesis for data augmentation. In this paper, we aim to improve multi-speaker end-to-end TTS systems to synthesize dysarthric speech for improved training of a dysarthria-specific DNN-HMM ASR. In the synthesized speech, we add dysarthria severity level and pause insertion mechanisms to other control parameters such as pitch, energy, and duration. Results show that a DNN-HMM model trained on additional synthetic dysarthric speech achieves WER improvement of 12.2% compared to the baseline, the addition of the severity level ... : Accepted ICASSP 2022 ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2201.11571 https://arxiv.org/abs/2201.11571
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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Giant Pigeon and Small Person: Prompting Visually Grounded Models about the Size of Objects ...
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Zhang, Yi. - : Purdue University Graduate School, 2022
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