2 |
Speech Synthesis from ECoG using Densely Connected 3D Convolutional Neural Networks
|
|
|
|
In: J Neural Eng (2019)
|
|
BASE
|
|
Show details
|
|
3 |
Generating Natural, Intelligible Speech From Brain Activity in Motor, Premotor, and Inferior Frontal Cortices
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Automatic Speech Recognition from Neural Signals: A Focused Review
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Brain-to-text: decoding spoken phrases from phone representations in the brain
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Multilingual Deep Neural Network based Acoustic Modeling For Rapid Language Adaptation
|
|
|
|
In: http://infoscience.epfl.ch/record/198446 (2014)
|
|
BASE
|
|
Show details
|
|
10 |
Integration of Language Identification into a Recognition System for Spoken Conversations Containing Code-Switches ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Integration of Language Identification into a Recognition System for Spoken Conversations Containing Code-Switches ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
An Investigation on Initialization Schemes for Multilayer Perceptron Training Using Multilingual Data and Their Effect on ASR Performance ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
An Investigation on Initialization Schemes for Multilayer Perceptron Training Using Multilingual Data and Their Effect on ASR Performance ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Multilingual Bottle-Neck Features and its Application for Under-Resourced Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Multilingual Bottle-Neck Features and its Application for Under-Resourced Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Modeling Coarticulation in EMG-based Continuous Speech Recognition
|
|
|
|
In: Speech Communication, 52 (4), 341-353 ; ISSN: 0167-6393 (2012)
|
|
Abstract:
This paper discusses the use of surface electromyography for automatic speech recognition. Electromyographic signals captured at the facial muscles record the activity of the human articulatory apparatus and thus allow to trace back a speech signal even if it is spoken silently. Since speech is captured before it gets airborne, the resulting signal is not masked by ambient noise. The resulting Silent Speech Interface has the potential to overcome major limitations of conventional speech-driven interfaces: it is not prone to any environmental noise, allows to silently transmit confidential information, and does not disturb bystanders. We describe our new approach of phonetic feature bundling for modeling coarticulation in EMG-based speech recognition and report results on the EMG-PIT corpus, a multiple speaker large vocabulary database of silent and audible EMG speech recordings, which we recently collected. Our results on speaker-dependent and speaker-independent setups show that modeling the interdependence of phonetic features reduces the word error rate of the baseline system by over 33% relative. Our final system achieves 10% word error rate for the best-recognized speaker on a 101-word vocabulary task, bringing EMG-based speech recognition within a useful range for the application of silent speech interfaces.
|
|
Keyword:
DATA processing & computer science; ddc:004; EMG-based Speech Recognition; info:eu-repo/classification/ddc/004; Phonetic Features; Silent Speech Interfaces
|
|
URL: https://publikationen.bibliothek.kit.edu/1000026321 https://publikationen.bibliothek.kit.edu/1000026321/7320014 http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:swb:90-263211 https://doi.org/10.5445/IR/1000026321
|
|
BASE
|
|
Hide details
|
|
|
|