Search Results for author:
Found 17 papers, 3 papers with code
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
Remixing-based Unsupervised Source Separation from Scratch
A student model is then trained to separate the pseudo-mixtures using either the teacher's outputs or the initial mixtures as supervision.
Neural Diarization with Non-autoregressive Intermediate Attractors
The experiments with the two-speaker CALLHOME dataset show that the intermediate labels with the proposed non-autoregressive intermediate attractors boost the diarization performance.
Video Surveillance System Incorporating Expert Decision-making Process: A Case Study on Detecting Calving Signs in Cattle
In this study, we examine the framework of a video surveillance AI system that presents the reasoning behind predictions by incorporating experts' decision-making processes with rich domain knowledge of the notification target.
Deep Multi-stream Network for Video-based Calving Sign Detection
We have designed a deep multi-stream network for automatically detecting calving signs from video.
Self-Remixing: Unsupervised Speech Separation via Separation and Remixing
Specifically, the shuffler first separates observed mixtures and makes pseudo-mixtures by shuffling and remixing the separated signals.
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
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
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).
Remix-cycle-consistent Learning on Adversarially Learned Separator for Accurate and Stable Unsupervised Speech Separation
A new learning algorithm for speech separation networks is designed to explicitly reduce residual noise and artifacts in the separated signal in an unsupervised manner.
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
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
Block-wise Scrambled Image Recognition Using Adaptation Network
In this study, a perceptually hidden object-recognition method is investigated to generate secure images recognizable by humans but not machines.
