All the recent advances in the field of artificial intelligence have allowed us to define intelligent systems with a better and more articulate understanding of language than ever before. With each update and release, large language models become more capable of meeting different needs in applications and scenarios. For any robust and efficient model, it is important to have a proper training indicator along with its design and content. Prompt engineering involves designing a prompt that allows the user to receive an appropriate response from the model. Its main goal is to feed the model a good quality training indicator so that the model can easily find patterns and trends in the data.
Focusing specifically on the domain of audio and speech processing, the study of rapid engineering has gained attention, but it is relatively new compared to other domains. The Whisper model, which was released by OpenAI, is a transformer-based encoder-decoder model that can be categorized into two groups: English-only and multilingual. Trained on a large dataset consisting of 680,000 hours of speech data pulled from the web, Whisper is an automatic speech recognition model.
In a recently published research paper, a team of researchers discussed adapting the Whisper model to invisible tasks using simple prompts. Called PromptingWhisper, the researchers’ primary focus has been to investigate the zero-shot task generalization abilities of the Whisper model by analyzing its strengths and weaknesses. To tailor Whisper for invisible tasks, the team has used prompt engineering to design task-specific prompts. They have mainly discussed three specific tasks, which are: Audiovisual Speech Recognition (AVSR), Code Switched Speech Recognition (CS-ASR), and Speech Translation (ST) involving invisible language pairs.
At AVSR, the team found that Whisper exhibited a robust nature in terms of visual cue duration and noise. Its effectiveness for visual cues in English models is different compared to multilingual models. In CS-ASR, some performance gaps were found between different accents. Lastly, in ST, it was found that the task token in prompts could be effectively used to tell the model to do the translation. To customize the prompts to the specific requirements of each task, the team manipulated the special tiles within the default prompts provided by Whisper or used another large-scale model.
The team has carried out experiments to evaluate the performance of the Whisper model. After comparing the default prompts with the proposed task-specific prompts, the results showed that their prompts significantly improved performance on all three zero-trigger tasks, with performance gains ranging from 10% to 45%. In some cases, the proposed indications even outperformed the models monitored by SOTA on certain data sets.
In conclusion, the researchers have investigated the Whisper model in great depth. During the evaluation, they looked at how Whisper is resilient to different cues, efficiently uncovers accent-related biases, and identifies the model’s ability to understand multiple languages within its latent space. They studied and analyzed Whisper’s hidden strengths and weaknesses in detail by focusing on the gradient-free zero-trigger task generalization abilities of web-scale speech models.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
