Search Results for author:
Found 71 papers, 36 papers with code
Conversation- and Tree-Structure Losses for Dialogue Disentanglement
When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately.
Sparsity-Driven EEG Channel Selection for Brain-Assisted Speech Enhancement
In this work, we therefore propose a novel end-to-end brain-assisted speech enhancement network (BASEN), which incorporates the listeners' EEG signals and adopts a temporal convolutional network together with a convolutional multi-layer cross attention module to fuse EEG-audio features.
APNet2: High-quality and High-efficiency Neural Vocoder with Direct Prediction of Amplitude and Phase Spectra
APNet demonstrates the capability to generate synthesized speech of comparable quality to the HiFi-GAN vocoder but with a considerably improved inference speed.
Is ChatGPT a Good Multi-Party Conversation Solver?
Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocutors involved in intricate information exchanges -- remains uncharted.
Untying the Reversal Curse via Bidirectional Language Model Editing
A new evaluation metric of reversibility is introduced, and a benchmark dubbed as Bidirectional Assessment for Knowledge Editing (BAKE) is constructed to evaluate the reversibility of edited models in recalling knowledge in the reverse direction of editing.
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement
Specifically, we guide an audio-lip speech enhancement student model to learn from a pre-trained audio-lip-tongue speech enhancement teacher model, thus transferring tongue-related knowledge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Face-Driven Zero-Shot Voice Conversion with Memory-based Face-Voice Alignment
This paper presents a novel task, zero-shot voice conversion based on face images (zero-shot FaceVC), which aims at converting the voice characteristics of an utterance from any source speaker to a newly coming target speaker, solely relying on a single face image of the target speaker.
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
Phase information has a significant impact on speech perceptual quality and intelligibility.
Incorporating Ultrasound Tongue Images for Audio-Visual Speech Enhancement through Knowledge Distillation
Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel.
MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation
Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead.
SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extraction
The proposed encoder is capable of interactively capturing complementary information between features and contextual information, to derive language-agnostic representations for various IE tasks.
DiffuSIA: A Spiral Interaction Architecture for Encoder-Decoder Text Diffusion
In fact, the encoder-decoder architecture is naturally more flexible for its detachable encoder and decoder modules, which is extensible to multilingual and multimodal generation tasks for conditions and target texts.
BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions
In this paper, we thus propose a novel time-domain brain-assisted SE network (BASEN) incorporating electroencephalography (EEG) signals recorded from the listener for extracting the target speaker from monaural speech mixtures.
GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding
Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention.
Zero-shot personalized lip-to-speech synthesis with face image based voice control
In this paper, we propose a zero-shot personalized Lip2Speech synthesis method, in which face images control speaker identities.
USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER
A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER.
Source-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis
This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by introducing F0-based source excitation signals to a neural filter framework.
Speech Reconstruction from Silent Tongue and Lip Articulation By Pseudo Target Generation and Domain Adversarial Training
This paper studies the task of speech reconstruction from ultrasound tongue images and optical lip videos recorded in a silent speaking mode, where people only activate their intra-oral and extra-oral articulators without producing sound.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition
In this study, a mixture of short-channel distillers (MSD) method is proposed to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Self-Supervised Audio-Visual Speech Representations Learning By Multimodal Self-Distillation
AV2vec has a student and a teacher module, in which the student performs a masked latent feature regression task using the multimodal target features generated online by the teacher.
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations
This paper proposes a multilingual speech synthesis method which combines unsupervised phonetic representations (UPR) and supervised phonetic representations (SPR) to avoid reliance on the pronunciation dictionaries of target languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TegTok) in a unified framework.
USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition
The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them.
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion.
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
We propose a novel Pooling Network (PoNet) for token mixing in long sequences with linear complexity.
Detecting Speaker Personas from Conversational Texts
This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences.
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models.
Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation
This paper presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses.
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing
The dynamic schema-state and SQL-state representations are then utilized to decode the SQL query corresponding to current utterance.
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
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots
The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously.
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to.
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots.
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7.
Ranked #1 on
Conversation Disentanglement
on irc-disentanglement
Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
We present our work on Track 4 in the Dialogue System Technology Challenges 8 (DSTC8).
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
The distances between context and response utterances are employed as a prior component when calculating the attention weights.
Ranked #7 on
Conversational Response Selection
on E-commerce
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
We also observe that fine-tuned models after the proposed pre-training approach maintain comparable performance on other NLP tasks, such as sentence classification and natural language inference tasks, compared to the original BERT models.
Ranked #21 on
Common Sense Reasoning
on CommonsenseQA
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates.
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).
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification.
Condition-Transforming Variational AutoEncoder for Conversation Response Generation
This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs).
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision.
Promoting Diversity for End-to-End Conversation Response Generation
At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task.
Ranked #6 on
Conversational Response Selection
on E-commerce
Learning latent representations for style control and transfer in end-to-end speech synthesis
In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised manner.
Building Sequential Inference Models for End-to-End Response Selection
This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7).
Ranked #5 on
Conversational Response Selection
on DSTC7 Ubuntu
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.
Enhancing Sentence Embedding with Generalized Pooling
This paper explores generalized pooling methods to enhance sentence embedding.
Ranked #10 on
Sentiment Analysis
on Yelp Fine-grained classification
Hybrid semi-Markov CRF for Neural Sequence Labeling
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing.
Ranked #62 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.
Natural Language Inference with External Knowledge
Modeling informal inference in natural language is very challenging.
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences
The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs.
Neural Natural Language Inference Models Enhanced with External Knowledge
With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.
Ranked #20 on
Natural Language Inference
on SNLI
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.
Ranked #71 on
Natural Language Inference
on SNLI
Natural Language Inference
Natural Language Understanding
+1
Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge
The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge.
Distraction-Based Neural Networks for Document Summarization
Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.
Enhanced LSTM for Natural Language Inference
Reasoning and inference are central to human and artificial intelligence.
Ranked #30 on
Natural Language Inference
on SNLI
Part-of-Speech Relevance Weights for Learning Word Embeddings
The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices.
Probabilistic Reasoning via Deep Learning: Neural Association Models
We propose to use neural networks to model association between any two events in a domain.
Integrate Document Ranking Information into Confidence Measure Calculation for Spoken Term Detection
The confidence measure of each term occurrence is then re-estimated through linear interpolation with the calculated document ranking weight to improve its reliability by integrating document-level information.
