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Found 10 papers, 2 papers with code
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition
We study the problem of compressing recurrent neural networks (RNNs).
NN-grams: Unifying neural network and n-gram language models for Speech Recognition
The model is trained using noise contrastive estimation (NCE), an approach that transforms the estimation problem of neural networks into one of binary classification between data samples and noise samples.
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model.
Ranked #42 on
Speech Recognition
on LibriSpeech test-clean
Language Modelling
Sequence-To-Sequence Speech Recognition
+1
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
Contextual automatic speech recognition, i. e., biasing recognition towards a given context (e. g. user's playlists, or contacts), is challenging in end-to-end (E2E) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer
Recurrent Neural Network Transducer (RNN-T), like most end-to-end speech recognition model architectures, has an implicit neural network language model (NNLM) and cannot easily leverage unpaired text data during training.
Neural-FST Class Language Model for End-to-End Speech Recognition
We propose Neural-FST Class Language Model (NFCLM) for end-to-end speech recognition, a novel method that combines neural network language models (NNLMs) and finite state transducers (FSTs) in a mathematically consistent framework.
Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models
Such a model allows us to achieve always-on ambient speech recognition on edge devices with low-memory neural processors.
Improved pronunciation prediction accuracy using morphology
Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems.
