Search Results for author:
Found 15 papers, 1 papers with code
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff
This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs).
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
On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment Analysis
This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA).
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
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.
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.
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
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.
