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Hits 81 – 100 of 11.324

81
Emotion Intensity and its Control for Emotional Voice Conversion ...
Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022
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82
Automatic Speech recognition for Speech Assessment of Preschool Children ...
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83
Deep Speech Based End-to-End Automated Speech Recognition (ASR) for Indian-English Accents ...
Dubey, Priyank; Shah, Bilal. - : arXiv, 2022
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84
KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics ...
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85
Automated speech tools for helping communities process restricted-access corpora for language revival efforts ...
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86
Learning English with Peppa Pig ...
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87
Separate What You Describe: Language-Queried Audio Source Separation ...
Liu, Xubo; Liu, Haohe; Kong, Qiuqiang. - : arXiv, 2022
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88
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition ...
Du, Ye-Qian; Zhang, Jie; Zhu, Qiu-Shi. - : arXiv, 2022
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89
Arabic Text-To-Speech (TTS) Data Preparation ...
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90
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 ...
Bilby, Mark G.. - : Zenodo, 2022
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91
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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92
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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93
Google Colab notebook ...
Bleaman, Isaac. - : figshare, 2022
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94
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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95
Google Colab notebook ...
Bleaman, Isaac. - : figshare, 2022
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96
REYD demo files ...
Bleaman, Isaac. - : figshare, 2022
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97
Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training ...
Abstract: Human speech data comprises a rich set of domain factors such as accent, syntactic and semantic variety, or acoustic environment. Previous work explores the effect of domain mismatch in automatic speech recognition between pre-training and fine-tuning as a whole but does not dissect the contribution of individual factors. In this paper, we present a controlled study to better understand the effect of such factors on the performance of pre-trained representations. To do so, we pre-train models either on modified natural speech or synthesized audio, with a single domain factor modified, and then measure performance on automatic speech recognition after fine tuning. Results show that phonetic domain factors play an important role during pre-training while grammatical and syntactic factors are far less important. To our knowledge, this is the first study to better understand the domain characteristics in self-supervised pre-training for speech. ... : Submitted to Insterspeech 2022 ...
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
URL: https://arxiv.org/abs/2203.00648
https://dx.doi.org/10.48550/arxiv.2203.00648
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98
Low-dimensional representation of infant and adult vocalization acoustics ...
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99
Dual-Decoder Transformer For end-to-end Mandarin Chinese Speech Recognition with Pinyin and Character ...
Yang, Zhao; Xi, Wei; Wang, Rui. - : arXiv, 2022
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100
Similarity and Content-based Phonetic Self Attention for Speech Recognition ...
Shim, Kyuhong; Sung, Wonyong. - : arXiv, 2022
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