Search Results for author:
Found 28 papers, 1 papers with code
Multi-Perspective Document Revision
To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document.
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation
The advance of generative models for images has inspired various training techniques for image recognition utilizing synthetic images.
SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition.
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model.
End-to-End Joint Target and Non-Target Speakers ASR
Target-speaker ASR systems are a promising way to only transcribe a target speaker's speech by enrolling the target speaker's information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Scheduled Sampling for Neural Transducer-based ASR
Experiments in three datasets confirm that RNNT trained with our SS approach achieves the best ASR performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture.
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss
In this work, we propose a new SE training criterion that minimizes the distance between clean and enhanced signals in the feature representation of the SSL model to alleviate the mismatch.
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models
However, since the two settings have been studied individually in general, there has been little research focusing on how effective a cross-lingual model is in comparison with a monolingual model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Leveraging Large Text Corpora for End-to-End Speech Summarization
The first technique is to utilize a text-to-speech (TTS) system to generate synthesized speech, which is used for E2E SSum training with the text summary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning
Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training.
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models
We investigate the performance on SUPERB while varying the structure and KD methods so as to keep the number of parameters constant; this allows us to analyze the contribution of the representation introduced by varying the model architecture.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.
Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages
To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition.
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling
Dialogue sequence labeling is a supervised learning task that estimates labels for each utterance in the target dialogue document, and is useful for many applications such as dialogue act estimation.
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning
We propose a semi-supervised learning method for building end-to-end rich transcription-style automatic speech recognition (RT-ASR) systems from small-scale rich transcription-style and large-scale common transcription-style datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation
To address this problem, we propose unified autoregressive modeling for joint end-to-end multi-talker overlapped ASR and speaker attribute estimation.
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition
However, the conventional method cannot take into account the relationships between these two different modal inputs because the input contexts are separately encoded for each modal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens
To execute multiple conversion tasks simultaneously without preparing matched datasets, our key idea is to distinguish individual conversion tasks using the on-off switch.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition
In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable.
Audio-Visual Speech Separation Using Cross-Modal Correspondence Loss
We present an audio-visual speech separation learning method that considers the correspondence between the separated signals and the visual signals to reflect the speech characteristics during training.
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation
We evaluate the effectiveness of the proposed model and proposed training method on Japanese discourse ASR tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
End-to-End Automatic Speech Recognition with Deep Mutual Learning
We also show that combining DML with the existing training techniques effectively improves ASR performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents
This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations.
MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training
However, these models require a large amount of paired data of spoken-style text and style normalized text, and it is difficult to prepare such a volume of data.
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model
Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data.
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling
In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets.
Neural Dialogue Context Online End-of-Turn Detection
This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances.
