Abstract: Today, modeling automatic speech recognition (ASR) systems using deep neural networks (DNNs) has led to considerable improvements in performance, ...

Abstract. Today, modeling automatic speech recognition (ASR) sys- tems using deep neural networks (DNNs) has led to con-.

Two novel approaches to DNN-based ASR are discussed and analyzed, the attention-based encoder–decoder approach, and the (segmental) inverted HMM approach, ...

Nov 19, 2015 · Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require ...

Sequence Modeling and Alignment for LVCSR-Systems. E Beck, A Zeyer, P Doetsch, A Merboldt, R Schlüter, H Ney. Proceedings of the 13. ITG Symposium on Speech ...

– Nonetheless, LVCSR systems will need around 60,000 triphones, which is a large enough number to pose challenges for model training. • First, the models add ...

The two word sequences are first aligned using a dynamic programming-based string alignment algorithm. After the alignment, the number of deletions (D) ...

Nov 18, 2020 · Merboldt, R. Schlüter, and. H. Ney, “Sequence modeling and alignment for LVCSR-systems,” in ITG Conference on Speech Communication, Oct ...

Ney: Sequence. Modeling and Alignment for LVCSR-Systems. In ITG Conference on Speech Communication,. Oldenburg, Oct. 2018. [Bengio & Ducharme+ 03] Y. Bengio ...

In this work, we compared a number of sequence-to-sequence modeling approaches on an LVCSR task. In experimental eval- uations, we find that the RNN ...