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Found 52 papers, 4 papers with code
A context-aware knowledge transferring strategy for CTC-based ASR
Non-autoregressive automatic speech recognition (ASR) modeling has received increasing attention recently because of its fast decoding speed and superior performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
HypR: A comprehensive study for ASR hypothesis revising with a reference corpus
In addition, we implement and compare several classic and representative methods, showing the recent research progress in revising speech recognition results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SquiggleFilter: An Accelerator for Portable Virus Detection
The MinION is a recent-to-market handheld nanopore sequencer.
Trompt: Towards a Better Deep Neural Network for Tabular Data
However, based on a recently published tabular benchmark, we can see deep neural networks still fall behind tree-based models on tabular datasets.
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Unsupervised discovery of acoustic tokens from audio corpora without annotation and learning vector representations for these tokens have been widely studied.
Learning to Distill: The Essence Vector Modeling Framework
The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition.
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
Word embedding methods revolve around learning continuous distributed vector representations of words with neural networks, which can capture semantic and/or syntactic cues, and in turn be used to induce similarity measures among words, sentences and documents in context.
Improved Spoken Document Summarization with Coverage Modeling Techniques
In addition to MMR, there is only a dearth of research concentrating on reducing redundancy or increasing diversity for the spoken document summarization task, as far as we are aware.
Leveraging Word Embeddings for Spoken Document Summarization
Owing to the rapidly growing multimedia content available on the Internet, extractive spoken document summarization, with the purpose of automatically selecting a set of representative sentences from a spoken document to concisely express the most important theme of the document, has been an active area of research and experimentation.
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable.
A neural document language modeling framework for spoken document retrieval
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on.
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering
In a spoken multiple-choice question answering (SMCQA) task, given a passage, a question, and multiple choices all in the form of speech, the machine needs to pick the correct choice to answer the question.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Investigation of Sentiment Controllable Chatbot
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences.
Speech Recognition by Simply Fine-tuning BERT
We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Non-autoregressive Transformer-based End-to-end ASR using BERT
Transformer-based models have led to significant innovation in classical and practical subjects as varied as speech processing, natural language processing, and computer vision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021
In this paper, inspired by the successes of visionlanguage pre-trained models and the benefits from training with adversarial attacks, we present a novel transformerbased cross-modal fusion modeling by incorporating the both notions for VQA challenge 2021.
A Flexible and Extensible Framework for Multiple Answer Modes Question Answering
Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks.
A BERT-based Siamese-structured Retrieval Model
Due to the development of deep learning, the natural language processing tasks have made great progresses by leveraging the bidirectional encoder representations from Transformers (BERT).
ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models
This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts.
ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information
In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information.
Chinese Movie Dialogue Question Answering Dataset
This paper also provides a baseline system and shows its performance on this dataset.
A Lexical-aware Non-autoregressive Transformer-based ASR Model
Non-autoregressive automatic speech recognition (ASR) has become a mainstream of ASR modeling because of its fast decoding speed and satisfactory result.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
