Search Results for author:
Found 12 papers, 7 papers with code
Stable Distillation: Regularizing Continued Pre-training for Low-Resource Automatic Speech Recognition
Specifically, first, we perform vanilla continued pre-training on an initial SSL pre-trained model on the target domain ASR dataset and call it the teacher.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning
Continued pre-training (CP) offers multiple advantages, like target domain adaptation and the potential to exploit the continuous stream of unlabeled data available online.
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
In this paper, we propose CompA, a collection of two expert-annotated benchmarks with a majority of real-world audio samples, to evaluate compositional reasoning in ALMs.
DeAR: Debiasing Vision-Language Models with Additive Residuals
These biases manifest as the skewed similarity between the representations for specific text concepts and images of people of different identity groups and, therefore, limit the usefulness of such models in real-world high-stakes applications.
UNFUSED: UNsupervised Finetuning Using SElf supervised Distillation
Unlike prior works, which directly fine-tune a self-supervised pre-trained encoder on a target dataset, we use the encoder to generate pseudo-labels for unsupervised fine-tuning before the actual fine-tuning step.
SLICER: Learning universal audio representations using low-resource self-supervised pre-training
We present a new Self-Supervised Learning (SSL) approach to pre-train encoders on unlabeled audio data that reduces the need for large amounts of labeled data for audio and speech classification.
MAST: Multiscale Audio Spectrogram Transformers
We present Multiscale Audio Spectrogram Transformer (MAST) for audio classification, which brings the concept of multiscale feature hierarchies to the Audio Spectrogram Transformer (AST).
Technology Pipeline for Large Scale Cross-Lingual Dubbing of Lecture Videos into Multiple Indian Languages
Cross-lingual dubbing of lecture videos requires the transcription of the original audio, correction and removal of disfluencies, domain term discovery, text-to-text translation into the target language, chunking of text using target language rhythm, text-to-speech synthesis followed by isochronous lipsyncing to the original video.
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition
Self-supervised learning (SSL) to learn high-level speech representations has been a popular approach to building Automatic Speech Recognition (ASR) systems in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning
Inspired by the recent progress in self-supervised learning for computer vision, in this paper we introduce DeLoRes, a new general-purpose audio representation learning approach.
DECAR: Deep Clustering for learning general-purpose Audio Representations
We introduce DECAR, a self-supervised pre-training approach for learning general-purpose audio representations.
Dual Script E2E framework for Multilingual and Code-Switching ASR
In the first system, the E2E model is trained on the CLS representation, and we use a novel data-driven back-end to recover the native language script.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
