Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition
In recent years, speech-based self-supervised learning (SSL) has made significant progress in various tasks, including automatic speech recognition (ASR). An ASR model with decent performance can be realized by fine-tuning an SSL model with a small fraction of labeled data. Reducing the demand for labeled data is always of great practical value. In this paper, we further extend the use of SSL to cut down labeling costs with active learning. Three types of units on different granularities are derived from speech signals in an unsupervised way, and their effects are compared by applying a contrastive data selection method. The experimental results show that our proposed data selection framework can effectively improve the word error rate (WER) by more than 11% with the same amount of labeled data, or halve the labeling cost while maintaining the same WER, compared to random selection.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)Self-Supervised Learningspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Compute-Efficient Active Learning
Active learning, a powerful paradigm in machine learning, aims at reducing labeling costs by selecting the most informative samples from an unlabeled dataset. However, the traditional active learning process often demand…
Active LearningPerturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning
This paper addresses the problem of optimizing the allocation of labeling resources for semi-supervised belief representation learning in social networks. The objective is to strategically identify valuable messages on s…
Active LearningData AugmentationGraph LearningRepresentation LearningBoosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples
The cost of annotating transcriptions for large speech corpora becomes a bottleneck to maximally enjoy the potential capacity of deep neural network-based automatic speech recognition models. In this paper, we present a …
Active LearningAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+1Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming
Easy to build and reliable machine learning models are what all data analysts want. Although machine learning is advancing daily, the labeling cost for supervised learning and the black-box nature of machine learning are…
Active LearningBIG-bench Machine Learningtext-classificationText Classification+1Active Testing: Sample-Efficient Model Evaluation
We introduce a new framework for sample-efficient model evaluation that we call active testing. While approaches like active learning reduce the number of labels needed for model training, existing literature largely ign…
Active LearningGaussian Processesmodel