在隐私约束下，用持续学习让语音模型学会9种印度语言而不遗忘。
A Study on Regularization-Based Continual Learning Methods for Indic ASR
- 基于正则化方法，逐步学习新语言，避免灾难性遗忘。
- 相比直接微调，词错误率降低12.3%，保留旧语言知识更优。
- 适合资源受限、数据分批到达的多语言语音系统开发。
印度的语言多样性给构建包容性自动语音识别（ASR）系统带来了巨大挑战。传统多语言模型需同时访问所有语言数据，但在数据分批到来且受隐私限制的现实场景下不切实际。持续学习（CL）通过允许模型按顺序学习新语言而避免灾难性遗忘，提供了可行方案。本文在IndicSUPERB基准的一个子集上研究了面向印度语言的持续学习方法。采用基于Conformer的混合RNN-T/CTC模型，初始在印地语上预训练，随后依次增量训练另外八种印度语言，共九种语言。评估了三种代表性正则化与知识蒸馏类持续学习策略：弹性权重巩固（EWC）、记忆感知突触（MAS）和无遗忘学习（LwF），这些方法适用于无重放、隐私敏感场景。通过清音和带噪数据上的词错误率（WER）以及反向迁移（Backward Transfer）衡量性能。还考察了每任务不同训练轮次（1、2、5、10）的影响。结果表明，与朴素微调相比，持续学习显著缓解遗忘，是应对真实约束下大规模多语言语音识别的有前景方法。代码已公开：https://github.com/FrozenWolf-Cyber/Indic-CL-ASR
原文摘要 · Abstract (English)
Indias linguistic diversity poses significant challenges for developing inclusive Automatic Speech Recognition (ASR) systems. Traditional multilingual models, which require simultaneous access to all language data, are impractical due to the sequential arrival of data and privacy constraints. Continual Learning (CL) offers a solution by enabling models to learn new languages sequentially without catastrophically forgetting previously learned knowledge. This paper investigates CL for ASR on Indian languages using a subset of the IndicSUPERB benchmark. We employ a Conformer-based hybrid RNN-T/CTC model, initially pretrained on Hindi, which is then incrementally trained on eight additional Indian languages, for a total sequence of nine languages. We evaluate three prominent regularization- and distillation-based CL strategies: Elastic Weight Consolidation (EWC), Memory Aware Synapses (MAS), and Learning without Forgetting (LwF), selected for their suitability in no-replay, privacy-conscious scenarios. Performance is analyzed using Word Error Rate (WER) for both RNN-T and CTC paths on clean and noisy data, as well as knowledge retention via Backward Transfer. We also explore the impact of varying the number of training epochs (1, 2, 5, and 10) per task. Results, compared against naive fine-tuning, demonstrate CLs effectiveness in mitigating forgetting, making it a promising approach for scalable ASR in diverse Indian languages under realistic constraints. The code is available at: https://github.com/FrozenWolf-Cyber/Indic-CL-ASR
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