Search Results for author:
Found 22 papers, 3 papers with code
Uncertainty Calibration for Deep Audio Classifiers
In this work, we investigate the uncertainty calibration for deep audio classifiers.
Adaptive Activation Network For Low Resource Multilingual Speech Recognition
The existing models mostly established a bottleneck (BN) layer by pre-training on a large source language, and transferring to the low resource target language.
Speech Augmentation Based Unsupervised Learning for Keyword Spotting
In our experiments, with augmentation based unsupervised learning, our KWS model achieves better performance than other unsupervised methods, such as CPC, APC, and MPC.
Self-Attention for Incomplete Utterance Rewriting
In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer instead of word embeddings and edit the original text accordingly to generate the complete utterance.
VU-BERT: A Unified framework for Visual Dialog
The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history.
ClsVC: Learning Speech Representations with two different classification tasks.
Voice conversion(VC) aims to convert one speaker's voice to generate a new speech as it is said by another speaker.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches.
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
We evaluated the proposed methods on phoneme classification and speaker recognition tasks.
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition
End-to-end speech recognition systems usually require huge amounts of labeling resource, while annotating the speech data is complicated and expensive.
Unidirectional Memory-Self-Attention Transducer for Online Speech Recognition
We propose a novel network structure, called Memory-Self-Attention (MSA) Transducer.
Joint Intent Detection And Slot Filling Based on Continual Learning Model
Slot filling and intent detection have become a significant theme in the field of natural language understanding.
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
To verify its universality over languages, we apply pre-trained models to solve low-resource speech recognition tasks in various spoken languages.
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder.
MelGlow: Efficient Waveform Generative Network Based on Location-Variable Convolution
In this paper, an efficient network, named location-variable convolution, is proposed to model the dependencies of waveforms.
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
The structure of our model are maintained concise to be implemented for real-time applications.
Large-scale Transfer Learning for Low-resource Spoken Language Understanding
However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data.
Automatic Speech Recognition
Spoken Language Understanding
+1
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit
Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together.
MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection
The MLNET leveraged multi-branches to extract multiple contextual speech information and investigated an effective attention block to weight the most crucial parts of the context for final classification.
Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model
The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition.
MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification
Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation.
GraphTTS: graph-to-sequence modelling in neural text-to-speech
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms.
AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel.
