55 research outputs found
Distributed Accelerated Projection-Based Consensus Decomposition
With the development of machine learning and Big Data, the concepts of linear
and non-linear optimization techniques are becoming increasingly valuable for
many quantitative disciplines. Problems of that nature are typically solved
using distinctive optimization algorithms, iterative methods, or heuristics. A
new variant of the Accelerated Projection-Based Consensus (APC) iterative
method is proposed, which is faster than its classical version while handling
large sparse matrices in distributed settings. The algorithm is proposed, and
its description and implementation in a high-level programming language are
presented. Convergence tests measuring acceleration factors based on real-world
datasets are done, and their results are promising. The results of this
research can be used as an alternative to solving numerical optimization
problems.Comment: Publicized in the TASK Quarterly scientific journal of the Gdansk
University of Technolog
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
Advanced neural network models have penetrated Automatic Speech Recognition
(ASR) in recent years, however, in language modeling many systems still rely on
traditional Back-off N-gram Language Models (BNLM) partly or entirely. The
reason for this are the high cost and complexity of training and using neural
language models, mostly possible by adding a second decoding pass (rescoring).
In our recent work we have significantly improved the online performance of a
conversational speech transcription system by transferring knowledge from a
Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with
text generation based data augmentation. In the present paper we analyze the
amount of transferable knowledge and demonstrate that the neural augmented LM
(RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by
dropping the second decoding pass and making the system real-time capable. We
also systematically compare word and subword LMs and show that subword-based
neural text augmentation can be especially beneficial in under-resourced
conditions. In addition, we show that using the RNN-BNLM in the first pass
followed by a neural second pass, offline ASR results can be even significantly
improved.Comment: 8 pages, 2 figures, accepted for publication at TSD 202
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
This paper presents an exploration of end-to-end automatic speech recognition
systems (ASR) for the largest open-source Russian language data set -- OpenSTT.
We evaluate different existing end-to-end approaches such as joint
CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with
the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model. For the
three available validation sets (phone calls, YouTube, and books), our best
end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%,
respectively. Under the same conditions, the hybridASR system demonstrates
33.5%, 20.9%, and 18.6% WER.Comment: Accepted by SPECOM 202
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