8 accepted papers
Existing theoretical work on Bayes-optimal fair classifiers usually considers a single (binary) sensitive feature. In practice, individuals are often defined by multiple sensitive features. In this paper, we characterize the Bayes-optimal fair classifier for multiple sensitive features under general
In this paper, we explore an alternate approach to the popular method of using large language models (LLMs) as a second decoder for Automated Speech Recognition (ASR) and speech understanding tasks. We propose to employ diffusion networks to generate a correction signal that can be applied on the or…
As large language models (LLMs) are increasingly being integrated into various real-world applications, the identification of their vulnerabilities to jailbreaking attacks becomes an essential component of ensuring the safety and reliability of LLMs. Previous studies have developed LLM assistants,…
In this work, we study the impact of Large-scale Language Models (LLM) on Automated Speech Recognition (ASR) of YouTube videos, which we use as a source for long-form ASR. We demonstrate up to 8% relative reduction in Word Error Eate (WER) on US English (en-us) and code-switched Indian English (en-i…
Regularization and data augmentation are crucial to training end-to-end automatic speech recognition systems. Dropout is a popular regularization technique, which operates on each neuron independently by multiplying it with a Bernoulli random variable. We propose a generalization of dropout, called…
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is time consuming and expensive to collect. Initializing the S2I model with an ASR model trained on copious speech data can…
With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition. In this paper we evaluate the usefulness of these proposed techniques on broa…
Recurrent neural networks have become increasingly popular for the task of language modeling achieving impressive gains in state-of-the-art speech recognition and natural language processing (NLP) tasks. Recurrent models exploit word dependencies over a much longer context window (as retained by the…