Search Results for author:
Found 17 papers, 1 papers with code
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF.
Instruction-Following Speech Recognition
Conventional end-to-end Automatic Speech Recognition (ASR) models primarily focus on exact transcription tasks, lacking flexibility for nuanced user interactions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
In this paper, we discuss two effective approaches to improve the efficiency and robustness of CLIP training: (1) augmenting the training dataset while maintaining the same number of optimization steps, and (2) filtering out samples that contain text regions in the image.
E2E Segmenter: Joint Segmenting and Decoding for Long-Form ASR
Improving the performance of end-to-end ASR models on long utterances ranging from minutes to hours in length is an ongoing challenge in speech recognition.
Unsupervised Data Selection via Discrete Speech Representation for ASR
Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving the fusion of acoustic and text representations in RNN-T
The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition
Our experiments show that for both losses, the WER on long-form speech reduces substantially as the training utterance length increases.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models
To attack RNN-T, we find prepending perturbation is more effective than the additive perturbation, and can mislead the models to predict the same short target on utterances of arbitrary length.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data
We propose a novel and effective learning method by leveraging a non-streaming ASR model as a teacher to generate transcripts on an arbitrarily large data set, which is then used to distill knowledge into streaming ASR models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Mean-Field Approximation to Gaussian-Softmax Integral with Application to Uncertainty Estimation
Many methods have been proposed to quantify the predictive uncertainty associated with the outputs of deep neural networks.
A Large Scale Speech Sentiment Corpus
We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium.
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task.
Hyper-parameter Tuning under a Budget Constraint
We study a budgeted hyper-parameter tuning problem, where we optimize the tuning result under a hard resource constraint.
Kernel Approximation Methods for Speech Recognition
First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection.
Learning Compact Recurrent Neural Networks
Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks.
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
