Enhancing Whisper’s Accuracy and Speed for Indian Languages through Prompt-Tuning and Tokenization
Year: 2025
Type: conference-paper
Abstract: Automatic speech recognition has recently seen a significant advancement with large foundational models such as Whisper. However, these models often struggle to perform well in low-resource languages, such as Indian languages. This paper explores two novel approaches to enhance Whisper’s multilingual speech recognition performance in Indian languages. First, we propose prompt-tuning with language family information, which enhances Whisper’s accuracy in linguistically similar languages. Second, we introduce a novel tokenizer that reduces the number of generated tokens, thereby accelerating Whisper’s inference speed. Our extensive experiments demonstrate that the tokenizer significantly reduces inference time, while prompt-tuning enhances accuracy across various Whisper model sizes, including Small, Medium, and Large. Together, these techniques achieve a balance between optimal WER and inference speed.
Authors: Kumud Tripathi, Raj Gothi, Pankaj Wasnik
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408345664