Search Results for author:
Found 33 papers, 8 papers with code
A Tree-Structured Decoder for Image-to-Markup Generation
Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup.
Adaptive Confidence Multi-View Hashing for Multimedia Retrieval
Furthermore, an adaptive confidence multi-view network is employed to measure the confidence of each view and then fuse multi-view features through a weighted summation.
Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction
For speech interaction, voice activity detection (VAD) is often used as a front-end.
CASA-ASR: Context-Aware Speaker-Attributed ASR
In addition, a two-pass decoding strategy is further proposed to fully leverage the contextual modeling ability resulting in a better recognition performance.
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD).
AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer
In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED.
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings
Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning
Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR
Speech enhancement (SE) is usually required as a front end to improve the speech quality in noisy environments, while the enhanced speech might not be optimal for automatic speech recognition (ASR) systems due to speech distortion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition
Unpaired data has shown to be beneficial for low-resource automatic speech recognition~(ASR), which can be involved in the design of hybrid models with multi-task training or language model dependent pre-training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition
The proposed approach explores both the complementarity of audio-visual modalities and long-term context dependency using a transformer-based fusion module and a flexible masking strategy.
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition
In this work, we therefore first analyze the noise robustness of wav2vec2. 0 via experiments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
By using the acoustic signals to train the network, respectively, we can build individual models for three tasks, whose parameters are averaged to obtain an average model, which is then used as the initialization for the BiLSTM model training of each task.
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition
In this paper, we propose a weakly supervised multilingual representation learning framework, called cross-lingual self-training (XLST).
Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention
In this paper, we propose a novel deep learning architecture to improving word-level lip-reading.
Correlating Subword Articulation with Lip Shapes for Embedding Aware Audio-Visual Speech Enhancement
We first extract visual embedding from lip frames using a pre-trained phone or articulation place recognizer for visual-only EASE (VEASE).
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer
In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Attentive Fusion Enhanced Audio-Visual Encoding for Transformer Based Robust Speech Recognition
The proposed method can sufficiently combine the two streams and weaken the over-reliance on the audio modality.
Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations
In this method, disentangled linguistic and speaker representations are extracted from acoustic features, and voice conversion is achieved by preserving the linguistic representations of source utterances while replacing the speaker representations with the target ones.
Audio and Speech Processing Sound
Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling
This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS).
Deep Neural Network Embeddings with Gating Mechanisms for Text-Independent Speaker Verification
In this paper, gating mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification.
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis.
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20% relative improvement compared to the LFR trained BLSTM.
Trajectory-based Radical Analysis Network for Online Handwritten Chinese Character Recognition
The RNN decoder aims at generating the caption by detecting radicals and spatial structures through an attention model.
Multi-Scale Attention with Dense Encoder for Handwritten Mathematical Expression Recognition
Handwritten mathematical expression recognition is a challenging problem due to the complicated two-dimensional structures, ambiguous handwriting input and variant scales of handwritten math symbols.
A GRU-based Encoder-Decoder Approach with Attention for Online Handwritten Mathematical Expression Recognition
In this study, we present a novel end-to-end approach based on the encoder-decoder framework with the attention mechanism for online handwritten mathematical expression recognition (OHMER).
Radical analysis network for zero-shot learning in printed Chinese character recognition
Chinese characters have a huge set of character categories, more than 20, 000 and the number is still increasing as more and more novel characters continue being created.
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals.
Sound
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA).
Ranked #39 on
Question Answering
on SQuAD1.1 dev
Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency
In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.
Feedforward Sequential Memory Neural Networks without Recurrent Feedback
We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback.
A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models
In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation.
