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Found 12 papers, 0 papers with code
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text
When mixing 20, 000 hours augmented speech data generated by our method with 12, 500 hours original transcribed speech data for Italian Transformer transducer model pre-training, we achieve 8. 7% relative word error rate reduction.
Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training
We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users during inference.
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers
In this paper, we propose LAMASSU, a streaming language-agnostic multilingual speech recognition and translation model using neural transducers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability
In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target language.
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes
Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Configurable Multilingual Model is All You Need to Recognize All Languages
Particularly, a single CMM can be deployed to any user scenario where the users can pre-select any combination of languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition
In this work, we perform LM fusion in the minimum WER (MWER) training of an E2E model to obviate the need for LM weights tuning during inference.
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition
The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal language model estimation (ILME) method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition
The external language models (LM) integration remains a challenging task for end-to-end (E2E) automatic speech recognition (ASR) which has no clear division between acoustic and language models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
High-Accuracy and Low-Latency Speech Recognition with Two-Head Contextual Layer Trajectory LSTM Model
While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion followed by a sequence discriminative training criterion, we argue that such conventional hybrid models can still be significantly improved.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Self-Teaching Networks
Furthermore, the auxiliary loss also works as a regularizer, which improves the generalization capacity of the network.
