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Found 13 papers, 1 papers with code
The 2016 KIT IWSLT Speech-to-Text Systems for English and German
For the English TED task, our best combination system has a WER of 7. 8% on the development set while our other combinations gained 21. 8% and 28. 7% WERs for the English and German MSLT tasks.
Towards Improving Low-Resource Speech Recognition Using Articulatory and Language Features
For system training, we use additional data from French, German and Turkish.
The 2017 KIT IWSLT Speech-to-Text Systems for English and German
For the English lecture task, our best combination system has a WER of 8. 3% on the tst2015 development set while our other combinations gained 25. 7% WER for German lecture tasks.
KIT’s IWSLT 2018 SLT Translation System
The baseline system is a cascade of an ASR system, a system to segment the ASR output and a neural machine translation system.
A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing
Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities.
Fluctuation driven transitions in localized insulators: Intermittent metallicity and path chaos precede delocalization
The first occurs between a non-resonating insulator and an intermittent metal.
Disordered Systems and Neural Networks Statistical Mechanics
Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding
In this paper, we treat an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space (CMLS) architecture, where a shared latent space is learned between the `acoustic' and `text' embeddings.
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Experiments show that for English-Spanish, the bilingual joint ASR-LID architecture matches monolingual ASR and acoustic-only LID accuracies.
Very Deep Self-Attention Networks for End-to-End Speech Recognition
Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community.
Neural Language Codes for Multilingual Acoustic Models
Multilingual Speech Recognition is one of the most costly AI problems, because each language (7, 000+) and even different accents require their own acoustic models to obtain best recognition performance.
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural networks (RNNs), trained using the Connectionist Temporal Classification (CTC) loss function.
Phonemic and Graphemic Multilingual CTC Based Speech Recognition
We evaluated the use of different language combinations as well as the addition of Language Feature Vectors (LFVs).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
BCs can be expressed in different ways, depending on the modality of the interaction, for example as gestures or acoustic cues.
