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Found 12 papers, 0 papers with code
Generative linguistic representation for spoken language identification
Effective extraction and application of linguistic features are central to the enhancement of spoken Language IDentification (LID) performance.
Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition
Multi-talker overlapped speech recognition remains a significant challenge, requiring not only speech recognition but also speaker diarization tasks to be addressed.
Neural domain alignment for spoken language recognition based on optimal transport
Our previous study discovered that completely aligning the distributions between the source and target domains can introduce a negative transfer, where classes or irrelevant classes from the source domain map to a different class in the target domain during distribution alignment.
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR
Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for automatic speech recognition (ASR) still remains a challenging task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Cross-modal Alignment with Optimal Transport for CTC-based ASR
Since the PLM is built from text while the acoustic model is trained with speech, a cross-modal alignment is required in order to transfer the context dependent linguistic knowledge from the PLM to acoustic encoding.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Pronunciation-aware unique character encoding for RNN Transducer-based Mandarin speech recognition
For Mandarin end-to-end (E2E) automatic speech recognition (ASR) tasks, compared to character-based modeling units, pronunciation-based modeling units could improve the sharing of modeling units in model training but meet homophone problems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Transducer-based language embedding for spoken language identification
The acoustic and linguistic features are important cues for the spoken language identification (LID) task.
Partial Coupling of Optimal Transport for Spoken Language Identification
In order to reduce domain discrepancy to improve the performance of cross-domain spoken language identification (SLID) system, as an unsupervised domain adaptation (UDA) method, we have proposed a joint distribution alignment (JDA) model based on optimal transport (OT).
Siamese Neural Network with Joint Bayesian Model Structure for Speaker Verification
However, in most of the discriminative training for SiamNN, only the distribution of pair-wised sample distances is considered, and the additional discriminative information in joint distribution of samples is ignored.
Coupling a generative model with a discriminative learning framework for speaker verification
By initializing the two-branch neural network with the generatively learned model parameters of the JB model, we train the model parameters with the pairwise samples as a binary discrimination task.
Unsupervised neural adaptation model based on optimal transport for spoken language identification
By minimizing the classification loss on the training data set with the adaptation loss on both training and testing data sets, the statistical distribution difference between training and testing domains is reduced.
Cross-scale Attention Model for Acoustic Event Classification
However, a potential limitation of the network is that the discriminative features from the bottom layers (which can model the short-range dependency) are smoothed out in the final representation.
