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1
A comparative study of several parameterizations for speaker recognition ...
Faundez-Zanuy, Marcos. - : arXiv, 2022
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2
Speaker verification in mismatch training and testing conditions ...
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3
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation ...
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4
A New Amharic Speech Emotion Dataset and Classification Benchmark ...
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5
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks ...
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6
The Norwegian Parliamentary Speech Corpus ...
Solberg, Per Erik; Ortiz, Pablo. - : arXiv, 2022
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7
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
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8
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects ...
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9
Automatic Dialect Density Estimation for African American English ...
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10
End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system ...
Zhang, Zhengyi; Zhou, Pan. - : arXiv, 2022
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11
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
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12
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation ...
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13
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations ...
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14
Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition ...
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15
Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
Abstract: Generating realistic lip motions to simulate speech production is key for driving natural character animations from audio. Previous research has shown that traditional metrics used to optimize and assess models for generating lip motions from speech are not a good indicator of subjective opinion of animation quality. Yet, running repetitive subjective studies for assessing the quality of animations can be time-consuming and difficult to replicate. In this work, we seek to understand the relationship between perturbed lip motion and subjective opinion of lip motion quality. Specifically, we adjust the degree of articulation for lip motion sequences and run a user-study to examine how this adjustment impacts the perceived quality of lip motion. We then train a model using the scores collected from our user-study to automatically predict the subjective quality of an animated sequence. Our results show that (1) users score lip motions with slight over-articulation the highest in terms of perceptual quality; (2) ... : Submitted to Interspeech 2022 ...
Keyword: Audio and Speech Processing eess.AS; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Graphics cs.GR; Sound cs.SD
URL: https://dx.doi.org/10.48550/arxiv.2203.10117
https://arxiv.org/abs/2203.10117
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16
Learning and controlling the source-filter representation of speech with a variational autoencoder ...
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17
Correcting Misproducted Speech using Spectrogram Inpainting ...
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18
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation ...
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19
Can Social Robots Effectively Elicit Curiosity in STEM Topics from K-1 Students During Oral Assessments? ...
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20
An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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