Search Results for author:
Found 84 papers, 2 papers with code
Building an Enhanced Autoregressive Document Retriever Leveraging Supervised Contrastive Learning
DSI dramatically simplifies the whole retrieval process by encoding all information about the document collection into the parameter space of a single Transformer model, on top of which DSI can in turn generate the relevant document identities (IDs) in an autoregressive manner in response to a user query.
A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students
Due to the surge in global demand for English as a second language (ESL), developments of automated methods for grading speaking proficiency have gained considerable attention.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Study on Contextualized Language Modeling for Machine Reading Comprehension
With the recent breakthrough of deep learning technologies, research on machine reading comprehension (MRC) has attracted much attention and found its versatile applications in many use cases.
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection
In the first stage, the speech uttered by an L2 learner is processed by an end-to-end ASR module to produce N-best phone sequence hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning
With the widespread commercialization of smart devices, research on environmental sound classification has gained more and more attention in recent years.
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution
Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition
End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks.
Speech-Aware Neural Diarization with Encoder-Decoder Attractor Guided by Attention Constraints
End-to-End Neural Diarization with Encoder-Decoder based Attractor (EEND-EDA) is an end-to-end neural model for automatic speaker segmentation and labeling.
An Effective Mixture-Of-Experts Approach For Code-Switching Speech Recognition Leveraging Encoder Disentanglement
With the massive developments of end-to-end (E2E) neural networks, recent years have witnessed unprecedented breakthroughs in automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition
In recent years, end-to-end speech recognition has emerged as a technology that integrates the acoustic, pronunciation dictionary, and language model components of the traditional Automatic Speech Recognition model.
Preserving Phonemic Distinctions for Ordinal Regression: A Novel Loss Function for Automatic Pronunciation Assessment
Automatic pronunciation assessment (APA) manages to quantify the pronunciation proficiency of a second language (L2) learner in a language.
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning
Voice, as input, has progressively become popular on mobiles and seems to transcend almost entirely text input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Hierarchical Context-aware Modeling Approach for Multi-aspect and Multi-granular Pronunciation Assessment
Automatic Pronunciation Assessment (APA) plays a vital role in Computer-assisted Pronunciation Training (CAPT) when evaluating a second language (L2) learner's speaking proficiency.
Geometric Learning of Hidden Markov Models via a Method of Moments Algorithm
We present a novel algorithm for learning the parameters of hidden Markov models (HMMs) in a geometric setting where the observations take values in Riemannian manifolds.
Effective Cross-Utterance Language Modeling for Conversational Speech Recognition
Conversational speech normally is embodied with loose syntactic structures at the utterance level but simultaneously exhibits topical coherence relations across consecutive utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis
In addition, we design and develop a pronunciation modeling network stacked on top of the NAR E2E models of our method to further boost the effectiveness of MD&D.
Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms
Recently, end-to-end (E2E) models, which allow to take spectral vector sequences of L2 (second-language) learners' utterances as input and produce the corresponding phone-level sequences as output, have attracted much research attention in developing mispronunciation detection (MD) systems.
Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech
End-to-end (E2E) neural models are increasingly attracting attention as a promising modeling approach for mispronunciation detection and diagnosis (MDD).
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR
In recent decades, many studies have suggested that phase information is crucial for speech enhancement (SE), and time-domain single-channel speech enhancement techniques have shown promise in noise suppression and robust automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
Due to the unprecedented breakthroughs brought about by deep learning, speech enhancement (SE) techniques have been developed rapidly and play an important role prior to acoustic modeling to mitigate noise effects on speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition
In view of this, we in this paper seek to represent the historical context information of an utterance as graph-structured data so as to distill cross-utterances, global word interaction relationships.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Innovative Bert-based Reranking Language Models for Speech Recognition
More recently, Bidirectional Encoder Representations from Transformers (BERT) was proposed and has achieved impressive success on many natural language processing (NLP) tasks such as question answering and language understanding, due mainly to its effective pre-training then fine-tuning paradigm as well as strong local contextual modeling ability.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The NTNU Taiwanese ASR System for Formosa Speech Recognition Challenge 2020
This paper describes the NTNU ASR system participating in the Formosa Speech Recognition Challenge 2020 (FSR-2020) supported by the Formosa Speech in the Wild project (FSW).
End-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms
Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features.
Query Expansion System for the VoxCeleb Speaker Recognition Challenge 2020
In this report, we describe our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020.
Effective FAQ Retrieval and Question Matching With Unsupervised Knowledge Injection
To this end, predominant approaches to FAQ retrieval typically rank question-answer pairs by considering either the similarity between the query and a question (q-Q), the relevance between the query and the associated answer of a question (q-A), or combining the clues gathered from the q-Q similarity measure and the q-A relevance measure.
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition
The attention-based encoder-decoder modeling paradigm has achieved promising results on a variety of speech processing tasks like automatic speech recognition (ASR), text-to-speech (TTS) and among others.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features
Tremendous amounts of multimedia associated with speech information are driving an urgent need to develop efficient and effective automatic summarization methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling
Mispronunciation detection and diagnosis (MDD) is a core component of computer-assisted pronunciation training (CAPT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
An Effective End-to-End Modeling Approach for Mispronunciation Detection
Recently, end-to-end (E2E) automatic speech recognition (ASR) systems have garnered tremendous attention because of their great success and unified modeling paradigms in comparison to conventional hybrid DNN-HMM ASR systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge
This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA.
What do you learn from context? Probing for sentence structure in contextualized word representations
The jiant toolkit for general-purpose text understanding models
Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018).
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling.
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.
