Search Results for author:
Found 14 papers, 5 papers with code
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models
Despite safety guardrails, experiments on jailbreaking demonstrate the vulnerability of SLMs to adversarial perturbations and transfer attacks, with average attack success rates of 90% and 10% respectively when evaluated on a dataset of carefully designed harmful questions spanning 12 different toxic categories.
SpeechVerse: A Large-scale Generalizable Audio Language Model
The models are instruction finetuned using continuous latent representations extracted from the speech foundation model to achieve optimal zero-shot performance on a diverse range of speech processing tasks using natural language instructions.
Retrieve and Copy: Scaling ASR Personalization to Large Catalogs
Personalization of automatic speech recognition (ASR) models is a widely studied topic because of its many practical applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages
A recent approach explores Contextual Adapters, wherein an attention-based biasing model for CTC is used to improve the recognition of custom entities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Don't Stop Self-Supervision: Accent Adaptation of Speech Representations via Residual Adapters
Speech representations learned in a self-supervised fashion from massive unlabeled speech corpora have been adapted successfully toward several downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale
Using a 66 billion parameter language model (OPT-66B) across a diverse set of 14 downstream tasks, we find this is indeed the case: $\sim$70% of attention heads and $\sim$20% of feed forward networks can be removed with minimal decline in task performance.
Towards Personalization of CTC Speech Recognition Models with Contextual Adapters and Adaptive Boosting
End-to-end speech recognition models trained using joint Connectionist Temporal Classification (CTC)-Attention loss have gained popularity recently.
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Prompt-tuning in ASR systems for efficient domain-adaptation
In this work, we overcome the problem using prompt-tuning, a methodology that trains a small number of domain token embedding parameters to prime a transformer-based LM to a particular domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Few Shot Dialogue State Tracking using Meta-learning
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc.
Robust Handwriting Recognition with Limited and Noisy Data
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved.
Covariate Distribution Aware Meta-learning
Based on this model we propose a computationally feasible meta-learning algorithm by introducing meaningful relaxations in our final objective.
Finding Input Characterizations for Output Properties in ReLU Neural Networks
Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains.
Restructuring Conversations using Discourse Relations for Zero-shot Abstractive Dialogue Summarization
Dialogue summarization is a challenging problem due to the informal and unstructured nature of conversational data.
Abstractive Dialogue Summarization
Abstractive Text Summarization
+1
