5 papers · 1 filter
Position-invariant Fine-tuning of Speech Enhancement Models with Self-supervised Speech Representations
Amit Meghanani, Thomas Hain
Integrating front-end speech enhancement (SE) models with self-supervised learning (SSL)-based speech models is effective for downstream tasks in noisy conditions. SE models are co…
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks
Amit Meghanani, Thomas Hain
Self-supervised learning (SSL)-based speech models are extensively used for full-stack speech processing. However, it has been observed that improving SSL-based speech representati…
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech Representations
Amit Meghanani, Thomas Hain
Acoustic word embeddings (AWEs) are vector representations of spoken words. An effective method for obtaining AWEs is the Correspondence Auto-Encoder (CAE). In the past, the CAE me…
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content Representations
Amit Meghanani, Thomas Hain
There is a growing interest in cost-effective self-supervised fine-tuning (SSFT) of self-supervised learning (SSL)-based speech models to obtain task-specific representations. Thes…
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition
Muhammad Umar Farooq, Rehan Ahmad, Thomas Hain
Student-teacher learning or knowledge distillation (KD) has been previously used to address data scarcity issue for training of speech recognition (ASR) systems. However, a limitat…