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Found 18 papers, 0 papers with code
Effective internal language model training and fusion for factorized transducer model
However, even with the adoption of factorized transducer models, limited improvement has been observed compared to shallow fusion.
AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition
Wearable devices like smart glasses are approaching the compute capability to seamlessly generate real-time closed captions for live conversations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision
Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16. 9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90, 000 hours).
Streaming Audio-Visual Speech Recognition with Alignment Regularization
In this work, we propose a streaming AV-ASR system based on a hybrid connectionist temporal classification (CTC)/attention neural network architecture.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+5
Anchored Speech Recognition with Neural Transducers
In this paper, we investigate anchored speech recognition to make neural transducers robust to background speech.
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition
The two most popular loss functions for streaming end-to-end automatic speech recognition (ASR) are RNN-Transducer (RNN-T) and connectionist temporal classification (CTC).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
As an example application, we use the extended GTC (GTC-e) for the multi-speaker speech recognition task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sequence Transduction with Graph-based Supervision
The recurrent neural network transducer (RNN-T) objective plays a major role in building today's best automatic speech recognition (ASR) systems for production.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy
Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to enhance the performance of end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition
Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition
MPL consists of a pair of online and offline models that interact and learn from each other, inspired by the mean teacher method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers
In this paper, we extend our prior work by (1) introducing the Conformer architecture to further improve the accuracy, (2) accelerating the decoding process with a novel activation recycling technique, and (3) enabling streaming decoding with triggered attention.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Capturing Multi-Resolution Context by Dilated Self-Attention
The restricted self-attention allows attention to neighboring frames of the query at a high resolution, and the dilation mechanism summarizes distant information to allow attending to it with a lower resolution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training
The performance of automatic speech recognition (ASR) systems typically degrades significantly when the training and test data domains are mismatched.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Semi-Supervised Speech Recognition via Graph-based Temporal Classification
However, alternative ASR hypotheses of an N-best list can provide more accurate labels for an unlabeled speech utterance and also reflect uncertainties of the seed ASR model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR
We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming automatic speech recognition with the transformer model
Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
