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Found 25 papers, 2 papers with code
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling
The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
CrossMap Transformer: A Crossmodal Masked Path Transformer Using Double Back-Translation for Vision-and-Language Navigation
Navigation guided by natural language instructions is particularly suitable for Domestic Service Robots that interacts naturally with users.
Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
Thus, inferring the collision-risk before a placing motion is crucial for achieving the requested task.
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.
Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary $F_{0}$ feature.
Alleviating the Burden of Labeling: Sentence Generation by Attention Branch Encoder-Decoder Network
Domestic service robots (DSRs) are a promising solution to the shortage of home care workers.
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation
In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG.
Audio and Speech Processing Sound
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.
A Multimodal Target-Source Classifier with Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects
To solve such a task, we propose the multimodal target-source classifier model with attention branches (MTCM-AB), which is an extension of the MTCM.
Multimodal Attention Branch Network for Perspective-Free Sentence Generation
In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots.
Incorporating Symbolic Sequential Modeling for Speech Enhancement
In this study, the symbolic sequences for acoustic signals are obtained as discrete representations with a Vector Quantized Variational Autoencoder algorithm.
A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions
This paper focuses on a multimodal language understanding method for carry-and-place tasks with domestic service robots.
Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification
The target task of this study is grounded language understanding for domestic service robots (DSRs).
End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks
For example, in measuring speech intelligibility, most of the evaluation metric is based on a short-time objective intelligibility (STOI) measure, while the frame based minimum mean square error (MMSE) between estimated and clean speech is widely used in optimizing the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Raw Waveform-based Speech Enhancement by Fully Convolutional Networks
Because the fully connected layers, which are involved in deep neural networks (DNN) and convolutional neural networks (CNN), may not accurately characterize the local information of speech signals, particularly with high frequency components, we employed fully convolutional layers to model the waveform.
