Search Results for author:
Found 17 papers, 4 papers with code
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Firstly, a generalized CTC loss function is introduced to handle noisy pseudo-labels by accepting alternative tokens in the positions of incorrect tokens.
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture
However, how to deploy hybrid CTC/attention systems for online speech recognition is still a non-trivial problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speech Corpora Divergence Based Unsupervised Data Selection for ASR
This study proposes a unsupervised target-aware data selection method based on speech corpora divergence (SCD), which can measure the similarity between two speech corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge
Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomenon between multilingual language and the frequent occurrence of code-switching phenomenon in daily life.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines
In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level.
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies
Firstly, we introduce a real-time encoder states revision strategy to modify previous states.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization
For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible.
Boosting Cross-Domain Speech Recognition with Self-Supervision
The cross-domain performance of automatic speech recognition (ASR) could be severely hampered due to the mismatch between training and testing distributions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Decoupled Federated Learning for ASR with Non-IID Data
Secondly, to reduce the communication and computation costs, we propose decoupled federated learning (DecoupleFL).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2. 0 models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models
The proposed NAR model significantly surpasses previous NAR systems on the AISHELL-1 benchmark and shows a potential for English tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition
Nevertheless, most of the previous SSL methods ignore the influence of the background noise or reverberation, which is crucial to deploying ASR systems in real-world speech applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR
In this work, in order to build a better pre-trained model for low-resource ASR, we propose a pre-training approach called wav2vec-S, where we use task-specific semi-supervised pre-training to refine the self-supervised pre-trained model for the ASR task thus more effectively utilize the capacity of the pre-trained model to generate task-specific representations for ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture
To support the online recognition, we integrate the state reuse chunk-SAE and the MTA based SAD into online CTC/attention architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation
The experimental results show that uPIT outperforms cPIT when LC-BLSTM is used during inference.
Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks
Time Delay Neural Networks (TDNNs), also known as onedimensional Convolutional Neural Networks (1-d CNNs), are an efficient and well-performing neural network architecture for speech recognition.
