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A Bottleneck Auto-Encoder for F0 Transformations on Speech and Singing Voice
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In: ISSN: 2078-2489 ; Information ; https://hal.archives-ouvertes.fr/hal-03599085 ; Information, MDPI, 2022, 13 (3), pp.102. ⟨10.3390/info13030102⟩ (2022)
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Neural Vocoding for Singing and Speaking Voices with the Multi-Band Excited WaveNet
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In: ISSN: 2078-2489 ; Information ; https://hal.archives-ouvertes.fr/hal-03599076 ; Information, MDPI, 2022, 13 (3), pp.103. ⟨10.3390/info13030103⟩ (2022)
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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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A comparative study of several parameterizations for speaker recognition ...
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Speaker verification in mismatch training and testing conditions ...
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Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation ...
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A New Amharic Speech Emotion Dataset and Classification Benchmark ...
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Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
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LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects ...
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Automatic Dialect Density Estimation for African American English ...
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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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Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations ...
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Abstract:
This paper presents a macroscopic approach to automatic detection of speech sound disorder (SSD) in child speech. Typically, SSD is manifested by persistent articulation and phonological errors on specific phonemes in the language. The disorder can be detected by focally analyzing the phonemes or the words elicited by the child subject. In the present study, instead of attempting to detect individual phone- and word-level errors, we propose to extract a subject-level representation from a long utterance that is constructed by concatenating multiple test words. The speaker verification approach, and posterior features generated by deep neural network models, are applied to derive various types of holistic representations. A linear classifier is trained to differentiate disordered speech in normal one. On the task of detecting SSD in Cantonese-speaking children, experimental results show that the proposed approach achieves improved detection performance over previous method that requires fusing phone-level ... : Submitted to Interspeech 2022 ...
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Keyword:
Audio and Speech Processing eess.AS; 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.2203.15405 https://arxiv.org/abs/2203.15405
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Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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Learning and controlling the source-filter representation of speech with a variational autoencoder ...
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Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation ...
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Can Social Robots Effectively Elicit Curiosity in STEM Topics from K-1 Students During Oral Assessments? ...
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Expression-preserving face frontalization improves visually assisted speech processing ...
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