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Hits 81 – 100 of 8.571

81
Freeform Body Motion Generation from Speech ...
Xu, Jing; Zhang, Wei; Bai, Yalong. - : arXiv, 2022
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82
Conceptual Modeling of Events Based on One-Category Ontology ...
Al-Fedaghi, Sabah. - : arXiv, 2022
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83
Linguistic-Acoustic Similarity Based Accent Shift for Accent Recognition ...
Shao, Qijie; Yan, Jinghao; Kang, Jian. - : arXiv, 2022
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84
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset ...
Yu, Tiezheng; Frieske, Rita; Xu, Peng. - : arXiv, 2022
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85
WavThruVec: Latent speech representation as intermediate features for neural speech synthesis ...
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86
Knowledge Transfer from Large-scale Pretrained Language Models to End-to-end Speech Recognizers ...
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87
The VoicePrivacy 2022 Challenge Evaluation Plan ...
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88
A Character-level Span-based Model for Mandarin Prosodic Structure Prediction ...
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89
Towards Property-Based Tests in Natural Language ...
Gordon, Colin S.. - : arXiv, 2022
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90
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition ...
Chen, Chengxin; Zhang, Pengyuan. - : arXiv, 2022
Abstract: Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association between state-of-the-art ASR models and the SER task has yet to be investigated. In this paper, we propose a novel channel and temporal-wise attention RNN (CTA-RNN) architecture based on the intermediate representations of pre-trained ASR models. Specifically, the embeddings of a large-scale pre-trained end-to-end ASR encoder contain both acoustic and linguistic information, as well as the ability to generalize to different speakers, making them well suited for downstream SER task. To further exploit the embeddings from different layers of the ASR encoder, we propose a novel CTA-RNN architecture to capture the emotional salient parts of embeddings in both the channel and temporal directions. We evaluate our approach on two popular benchmark datasets, IEMOCAP and MSP-IMPROV, using both within-corpus and cross-corpus settings. ... : 5 pages, 2 figures, submitted to INTERSPEECH 2022 ...
Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.2203.17023
https://arxiv.org/abs/2203.17023
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91
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model ...
Shen, Ying; Yang, Huiyu; Lin, Lin. - : arXiv, 2022
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92
Fine-grained Noise Control for Multispeaker Speech Synthesis ...
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93
Emotion Intensity and its Control for Emotional Voice Conversion ...
Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022
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94
Automatic Speech recognition for Speech Assessment of Preschool Children ...
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95
The HCCL-DKU system for fake audio generation task of the 2022 ICASSP ADD Challenge ...
Chen, Ziyi; Hua, Hua; Zhang, Yuxiang. - : arXiv, 2022
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96
Dawn of the transformer era in speech emotion recognition: closing the valence gap ...
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97
Deep Speech Based End-to-End Automated Speech Recognition (ASR) for Indian-English Accents ...
Dubey, Priyank; Shah, Bilal. - : arXiv, 2022
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98
KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics ...
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99
Automated speech tools for helping communities process restricted-access corpora for language revival efforts ...
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100
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach ...
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