10 citations · 25 across the 9 of their papers we have counts for
10 papers
Super-Human Performance in Online Low-latency Recognition of Conversational Speech
Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
Achieving super-human performance in recognizing human speech has been a goal for several decades, as researchers have worked on increasingly challenging tasks. In the 1990's it wa…
ELITR Non-Native Speech Translation at IWSLT 2020
Dominik Macháček, Jonáš Kratochvíl, Sangeet Sagar +6
This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to…
Relative Positional Encoding for Speech Recognition and Direct Translation
Ngoc-Quan Pham, Thanh-Le Ha, Tuan-Nam Nguyen +5
Transformer models are powerful sequence-to-sequence architectures that are capable of directly mapping speech inputs to transcriptions or translations. However, the mechanism for…
Low Latency ASR for Simultaneous Speech Translation
Thai Son Nguyen, Jan Niehues, Eunah Cho +6
User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques f…
Toward Cross-Domain Speech Recognition with End-to-End Models
Thai-Son Nguyen, Sebastian Stüker, Alex Waibel
In the area of multi-domain speech recognition, research in the past focused on hybrid acoustic models to build cross-domain and domain-invariant speech recognition systems. In thi…
High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
Thai-Son Nguyen, Ngoc-Quan Pham, Sebastian Stueker +1
Recently sequence-to-sequence models have started to achieve state-of-the-art performance on standard speech recognition tasks when processing audio data in batch mode, i.e., the c…
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation
Thai-Son Nguyen, Sebastian Stueker, Jan Niehues +1
Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains…
Very Deep Self-Attention Networks for End-to-End Speech Recognition
Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues +3
Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve arou…
Learning Shared Encoding Representation for End-to-End Speech Recognition Models
Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
In this work, we learn a shared encoding representation for a multi-task neural network model optimized with connectionist temporal classification (CTC) and conventional framewise…
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models
Thai-Son Nguyen, Sebastian Stueker, Alex Waibel
Acoustic-to-word (A2W) models that allow direct mapping from acoustic signals to word sequences are an appealing approach to end-to-end automatic speech recognition due to their si…