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Found 27 papers, 3 papers with code
Chain of Correction for Full-text Speech Recognition with Large Language Models
Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) has gained increased attention due to its potential to correct errors across long contexts and address a broader spectrum of error types, including punctuation restoration and inverse text normalization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Full-text Error Correction for Chinese Speech Recognition with Large Language Model
This paper investigates the effectiveness of LLMs for error correction in full-text generated by ASR systems from longer speech recordings, such as transcripts from podcasts, news broadcasts, and meetings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+8
Pinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language Models
Firstly, we construct a specialized benchmark dataset aimed at error correction for Chinese ASR with 724K hypotheses-transcription pairs, named the Chinese Hypotheses Paradise dataset (ChineseHP), which contains a wide range of scenarios and presents significant challenges.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Two-Stage Framework to Generate Video Chapter
On top of this dataset, we propose a two-stage framework to perform chapter localization and chapter title generation.
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition
Augmentation related issues, such as comparison of different strategies and ratios for data combination are also investigated.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Can We Trust Deep Speech Prior?
Recently, speech enhancement (SE) based on deep speech prior has attracted much attention, such as the variational auto-encoder with non-negative matrix factorization (VAE-NMF) architecture.
AP20-OLR Challenge: Three Tasks and Their Baselines
Based on Kaldi and Pytorch, recipes for i-vector and x-vector systems are also conducted as baselines for the three tasks.
AP19-OLR Challenge: Three Tasks and Their Baselines
The participants can refer to these online-published recipes to deploy LID systems for convenience.
Gaussian-Constrained training for speaker verification
This paper proposes a Gaussian-constrained training approach that (1) discards the parametric classifier, and (2) enforces the distribution of the derived speaker vectors to be Gaussian.
Phonetic-attention scoring for deep speaker features in speaker verification
This score reflects the similarity of the two frames in phonetic content, and is used to weigh the contribution of this frame pair in the utterance-based scoring.
AP18-OLR Challenge: Three Tasks and Their Baselines
The third oriental language recognition (OLR) challenge AP18-OLR is introduced in this paper, including the data profile, the tasks and the evaluation principles.
Deep factorization for speech signal
Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors.
Human and Machine Speaker Recognition Based on Short Trivial Events
Trivial events are ubiquitous in human to human conversations, e. g., cough, laugh and sniff.
Full-info Training for Deep Speaker Feature Learning
In recent studies, it has shown that speaker patterns can be learned from very short speech segments (e. g., 0. 3 seconds) by a carefully designed convolutional & time-delay deep neural network (CT-DNN) model.
AP17-OLR Challenge: Data, Plan, and Baseline
We present the data profile and the evaluation plan of the second oriental language recognition (OLR) challenge AP17-OLR.
Deep Speaker Verification: Do We Need End to End?
This principle has recently been applied to several prototype research on speaker verification (SV), where the feature learning and classifier are learned together with an objective function that is consistent with the evaluation metric.
Deep Factorization for Speech Signal
In this paper, we demonstrated that the speaker factor is also a short-time spectral pattern and can be largely identified with just a few frames using a simple deep neural network (DNN).
Deep Speaker Feature Learning for Text-independent Speaker Verification
Recently deep neural networks (DNNs) have been used to learn speaker features.
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID).
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID).
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition
Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Collaborative Learning for Language and Speaker Recognition
This paper presents a unified model to perform language and speaker recognition simultaneously and altogether.
Multi-task Recurrent Model for True Multilingual Speech Recognition
Research on multilingual speech recognition remains attractive yet challenging.
OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline
We present the OC16-CE80 Chinese-English mixlingual speech database which was released as a main resource for training, development and test for the Chinese-English mixlingual speech recognition (MixASR-CHEN) challenge on O-COCOSDA 2016.
Multi-task Recurrent Model for Speech and Speaker Recognition
Although highly correlated, speech and speaker recognition have been regarded as two independent tasks and studied by two communities.
Knowledge Transfer Pre-training
Compared to the conventional layer-wise methods, this new method does not care about the model structure, so can be used to pre-train very complex models.
Recurrent Neural Network Training with Dark Knowledge Transfer
Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
