23 citations · 23 across the 3 of their papers we have counts for
7 papers
Tight Integrated End-to-End Training for Cascaded Speech Translation
Parnia Bahar, Tobias Bieschke, Ralf Schlüter +1
A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation b…
Two-Way Neural Machine Translation: A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
Parnia Bahar, Christopher Brix, Hermann Ney
Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not e…
Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
Christopher Brix, Parnia Bahar, Hermann Ney
Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computa…
On using 2D sequence-to-sequence models for speech recognition
Parnia Bahar, Albert Zeyer, Ralf Schlüter +1
Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition. Using these architectures, one-dimensional input and output sequences are…
On Using SpecAugment for End-to-End Speech Translation
Parnia Bahar, Albert Zeyer, Ralf Schlüter +1
This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation. SpecAugment is a low-cost implementation method applied directly to the…
A Comparative Study on End-to-end Speech to Text Translation
Parnia Bahar, Tobias Bieschke, Hermann Ney
Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an o…