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Found 19 papers, 10 papers with code
Neural Speech Translation at AppTek
This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks.
Monotonic segmental attention for automatic speech recognition
We restrict the decoder attention to segments to avoid quadratic runtime of global attention, better generalize to long sequences, and eventually enable streaming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
Why does CTC result in peaky behavior?
The peaky behavior of CTC models is well known experimentally.
Equivalence of Segmental and Neural Transducer Modeling: A Proof of Concept
With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a number of modeling architectures like encoder-decoder attention models, transducer models and segmental models (direct HMM).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions.
Librispeech Transducer Model with Internal Language Model Prior Correction
We present our transducer model on Librispeech.
Ranked #25 on
Speech Recognition
on LibriSpeech test-clean
(using extra training data)
A study of latent monotonic attention variants
We compare several monotonic latent models to our global soft attention baseline such as a hard attention model, a local windowed soft attention model, and a segmental soft attention model.
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e. g. byte-pair encoding (BPE).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A New Training Pipeline for an Improved Neural Transducer
We compare the original training criterion with the full marginalization over all alignments, to the commonly used maximum approximation, which simplifies, improves and speeds up our training.
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems
We achieve improvements of up to 33% relative in word-error-rate (WER) over a strong baseline with data-augmentation in a low-resource environment (LibriSpeech-100h), closing the gap to a comparable oracle experiment by more than 50\%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
On Using SpecAugment for End-to-End Speech Translation
This work investigates a simple data augmentation technique, SpecAugment, for end-to-end speech translation.
On using 2D sequence-to-sequence models for speech recognition
Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Language Modeling with Deep Transformers
We explore deep autoregressive Transformer models in language modeling for speech recognition.
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
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.
Ranked #24 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder.
Improved training of end-to-end attention models for speech recognition
Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition.
Ranked #43 on
Speech Recognition
on LibriSpeech test-clean
(using extra training data)
RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks
In this work we release our extensible and easily configurable neural network training software.
A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition
On this task, we get our best result with an 8 layer bidirectional LSTM and we show that a pretraining scheme with layer-wise construction helps for deep LSTMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
