DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...50
Hits 1 – 20 of 989

1
A comparative study of several parameterizations for speaker recognition ...
Faundez-Zanuy, Marcos. - : arXiv, 2022
BASE
Show details
2
Speaker verification in mismatch training and testing conditions ...
BASE
Show details
3
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation ...
BASE
Show details
4
A New Amharic Speech Emotion Dataset and Classification Benchmark ...
BASE
Show details
5
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks ...
BASE
Show details
6
The Norwegian Parliamentary Speech Corpus ...
Solberg, Per Erik; Ortiz, Pablo. - : arXiv, 2022
BASE
Show details
7
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ...
BASE
Show details
8
LPC Augment: An LPC-Based ASR Data Augmentation Algorithm for Low and Zero-Resource Children's Dialects ...
BASE
Show details
9
Automatic Dialect Density Estimation for African American English ...
BASE
Show details
10
End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system ...
Zhang, Zhengyi; Zhou, Pan. - : arXiv, 2022
BASE
Show details
11
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems ...
BASE
Show details
12
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation ...
Abstract: Speech translation models are unable to directly process long audios, like TED talks, which have to be split into shorter segments. Speech translation datasets provide manual segmentations of the audios, which are not available in real-world scenarios, and existing segmentation methods usually significantly reduce translation quality at inference time. To bridge the gap between the manual segmentation of training and the automatic one at inference, we propose Supervised Hybrid Audio Segmentation (SHAS), a method that can effectively learn the optimal segmentation from any manually segmented speech corpus. First, we train a classifier to identify the included frames in a segmentation, using speech representations from a pre-trained wav2vec 2.0. The optimal splitting points are then found by a probabilistic Divide-and-Conquer algorithm that progressively splits at the frame of lowest probability until all segments are below a pre-specified length. Experiments on MuST-C and mTEDx show that the translation of ... : Submitted to Interspeech 2022, 5 pages. Previous version (v1) has additionally a 2-page Appendix ...
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/2202.04774
https://dx.doi.org/10.48550/arxiv.2202.04774
BASE
Hide details
13
Automatic Detection of Speech Sound Disorder in Child Speech Using Posterior-based Speaker Representations ...
BASE
Show details
14
Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition ...
BASE
Show details
15
Telepractice treatment of rhotics (Peterson et al., 2022) ...
BASE
Show details
16
Telepractice treatment of rhotics (Peterson et al., 2022) ...
BASE
Show details
17
Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
BASE
Show details
18
Learning and controlling the source-filter representation of speech with a variational autoencoder ...
BASE
Show details
19
Correcting Misproducted Speech using Spectrogram Inpainting ...
BASE
Show details
20
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals ...
BASE
Show details

Page: 1 2 3 4 5...50

Catalogues
0
0
0
0
0
0
0
Bibliographies
0
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
989
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern