Search Results for author:
Found 19 papers, 2 papers with code
Phrase-Level Localization of Inconsistency Errors in Summarization by Weak Supervision
Although the fluency of automatically generated abstractive summaries has improved significantly with advanced methods, the inconsistency that remains in summarization is recognized as an issue to be addressed.
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target speaker extraction (TSE), and speaker counting.
Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition
We present a novel integration of an instruction-tuned large language model (LLM) and end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder
One crucial factor that makes this integration challenging lies in the vocabulary mismatch; the vocabulary constructed for a pre-trained LM is generally too large for E2E-ASR training and is likely to have a mismatch against a target ASR domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss
This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC).
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR
In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units
In this work, to promote the word-level representation learning in end-to-end ASR, we propose a hierarchical conditional model that is based on connectionist temporal classification (CTC).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System
As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased.
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification
Recognition of the mental state of a human character in text is a major challenge in natural language processing.
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.
Audio and Speech Processing Sound
Word Attribute Prediction Enhanced by Lexical Entailment Tasks
Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes.
Towards Answer-unaware Conversational Question Generation
Conversational question generation is a novel area of NLP research which has a range of potential applications.
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension
However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions.
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations
This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words.
