6 papers · 1 filter
Latent Space Factorization in LoRA
Shashi Kumar, Yacouba Kaloga, John Mitros +2
Low-rank adaptation (LoRA) is a widely used method for parameter-efficient finetuning. However, existing LoRA variants lack mechanisms to explicitly disambiguate task-relevant info…
TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Shashi Kumar, Srikanth Madikeri, Esaú Villatoro-Tello +8
Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets a…
Unifying Streaming and Non-streaming Zipformer-based ASR
Bidisha Sharma, Karthik Pandia Durai, Shankar Venkatesan +4
There has been increasing interest in unifying streaming and non-streaming automatic speech recognition (ASR) models to reduce development, training, and deployment costs. We prese…
Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
Andres Carofilis, Pradeep Rangappa, Srikanth Madikeri +10
Fine-tuning pretrained ASR models for specific domains is challenging when labeled data is scarce. But unlabeled audio and labeled data from related domains are often available. We…
Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering
Pradeep Rangappa, Andres Carofilis, Jeena Prakash +10
Fine-tuning pretrained ASR models for specific domains is challenging for small organizations with limited labeled data and computational resources. Here, we explore different data…
A Differentiable Alignment Framework for Sequence-to-Sequence Modeling via Optimal Transport
Yacouba Kaloga, Shashi Kumar, Petr Motlicek +1
Accurate sequence-to-sequence (seq2seq) alignment is critical for applications like medical speech analysis and language learning tools relying on automatic speech recognition (ASR…