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Found 10 papers, 0 papers with code
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition.
Ranked #23 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Large scale weakly and semi-supervised learning for low-resource video ASR
Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
We compare the transformer based acoustic models with their LSTM counterparts on industrial scale tasks.
Contrastive Semi-supervised Learning for ASR
Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency
DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.
Flexi-Transducer: Optimizing Latency, Accuracy and Compute forMulti-Domain On-Device Scenarios
In order to achieve flexible and better accuracy and latency trade-offs, the following techniques are used.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
Streaming Transformer Transducer Based Speech Recognition Using Non-Causal Convolution
This paper improves the streaming transformer transducer for speech recognition by using non-causal convolution.
Scaling ASR Improves Zero and Few Shot Learning
With 4. 5 million hours of English speech from 10 different sources across 120 countries and models of up to 10 billion parameters, we explore the frontiers of scale for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions
This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
