Search Results for author:
Found 12 papers, 3 papers with code
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition
Data2vec is a self-supervised learning (SSL) approach that employs a teacher-student architecture for contextual representation learning via masked prediction, demonstrating remarkable performance in monolingual ASR.
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training
NEST-RQ employs causal encoders with only left context and uses next token prediction (NTP) as the training task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
We also observe the cross-modal representation extracted by CIF-PT obtains better performance than other neural interfaces for the tasks of SLU, including the dominant speech representation learned from self-supervised pre-training.
Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection
Nowadays, most methods in end-to-end contextual speech recognition bias the recognition process towards contextual knowledge.
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition
End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream.
A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition
End-to-end models are gaining wider attention in the field of automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition
In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction.
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping
Experiments on two Mandarin ASR datasets show the replacement of RNNs by the self-attention networks yields a 8. 4%-10. 2% relative character error rate (CER) reduction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
End-to-end models have been showing superiority in Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese
Experiments on HKUST datasets demonstrate that the lexicon free modeling units can outperform lexicon related modeling units in terms of character error rate (CER).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Furthermore, we investigate a comparison between syllable based model and context-independent phoneme (CI-phoneme) based model with the Transformer in Mandarin Chinese.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
