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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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Abstract:
End-to-end (E2E) speech recognition architectures assemble all components of traditional speech recognition system into a single model. Although it simplifies ASR system, it introduces contextual ASR drawback: the E2E model has worse performance on utterances containing infrequent proper nouns. In this work, we propose to add a contextual bias attention (CBA) module to attention based encoder decoder (AED) model to improve its ability of recognizing the contextual phrases. Specifically, CBA utilizes the context vector of source attention in decoder to attend to a specific bias embedding. Jointly learned with the basic AED parameters, CBA can tell the model when and where to bias its output probability distribution. At inference stage, a list of bias phrases is preloaded and we adapt the posterior distributions of both CTC and attention decoder according to the attended bias phrase of CBA. We evaluate the proposed method on GigaSpeech and achieve a consistent relative improvement on recall rate of bias ... : 5 pages, 5 tabels, 1 figure ...
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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://arxiv.org/abs/2202.09003 https://dx.doi.org/10.48550/arxiv.2202.09003
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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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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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