35 research outputs found
Efficient Training of Neural Transducer for Speech Recognition
As one of the most popular sequence-to-sequence modeling approaches for
speech recognition, the RNN-Transducer has achieved evolving performance with
more and more sophisticated neural network models of growing size and
increasing training epochs. While strong computation resources seem to be the
prerequisite of training superior models, we try to overcome it by carefully
designing a more efficient training pipeline. In this work, we propose an
efficient 3-stage progressive training pipeline to build highly-performing
neural transducer models from scratch with very limited computation resources
in a reasonable short time period. The effectiveness of each stage is
experimentally verified on both Librispeech and Switchboard corpora. The
proposed pipeline is able to train transducer models approaching
state-of-the-art performance with a single GPU in just 2-3 weeks. Our best
conformer transducer achieves 4.1% WER on Librispeech test-other with only 35
epochs of training.Comment: accepted at Interspeech 202
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
We present state-of-the-art automatic speech recognition (ASR) systems
employing a standard hybrid DNN/HMM architecture compared to an attention-based
encoder-decoder design for the LibriSpeech task. Detailed descriptions of the
system development, including model design, pretraining schemes, training
schedules, and optimization approaches are provided for both system
architectures. Both hybrid DNN/HMM and attention-based systems employ
bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we
employ both LSTM and Transformer based architectures. All our systems are built
using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the
authors, the results obtained when training on the full LibriSpeech training
set, are the best published currently, both for the hybrid DNN/HMM and the
attention-based systems. Our single hybrid system even outperforms previous
results obtained from combining eight single systems. Our comparison shows that
on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the
attention-based system by 15% relative on the clean and 40% relative on the
other test sets in terms of word error rate. Moreover, experiments on a reduced
100h-subset of the LibriSpeech training corpus even show a more pronounced
margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201
New law on custody over adults
The law on custody over adults in force in the Federal Republic of Germany
is the expression of the XIXth c. ideas. Presently, legislative works on the
amendment of the said law are in progress. They aim at taking into account to
a greater extent a humane aspect and at making legal provisions more flexible,
thus making it possible to adjust legal procedure and a court decree to an
individualised situation of a handicapped person.Digitalizacja i deponowanie archiwalnych zeszytów RPEiS sfinansowane przez MNiSW w ramach realizacji umowy nr 541/P-DUN/201