Leveraging Whisper's Robust Speech Recognition for Predicting Microscopic Intelligibility of Noisy Speech

Core Concepts

Transfer learning from the state-of-the-art Whisper automatic speech recognition model can effectively predict the distribution of lexical responses perceived by human listeners to noisy speech stimuli, outperforming baseline methods.

Abstract

The paper explores the use of transfer learning from the Whisper automatic speech recognition (ASR) model to the task of microscopic intelligibility prediction. Microscopic intelligibility models aim to predict fine-grained details of human speech perception, such as the specific lexical responses listeners perceive for a given noisy speech stimulus.
The authors use the English Consistent Confusion Corpus (ECCC) dataset, which contains words-in-noise misperceived in the same way by at least 6 out of 15 listeners. They evaluate their method on the task of predicting the full distribution of lexical responses, which is considered the most challenging microscopic intelligibility prediction task.
The key findings are:

The authors' method, which leverages transfer learning from the pre-trained Whisper model, significantly outperforms baseline approaches, even in a zero-shot setup without fine-tuning.
Fine-tuning the Whisper model to directly predict the distribution of listeners' responses leads to further performance gains, with a relative improvement of up to 66% over the baselines.
The largest performance improvements come from fine-tuning the convolutional encoder layers of Whisper, suggesting that low-level acoustic features are crucial for capturing human speech perception.
Larger Whisper models exhibit better performance, indicating that increased accuracy and robustness of the pre-trained model translates to improved microscopic intelligibility prediction.
The model struggles the most with speech corrupted by four-speaker babble noise, suggesting limitations in capturing the complex effects of this type of masker on human perception.
Overall, the results showcase the promise of leveraging large-scale deep learning models like Whisper for microscopic intelligibility prediction, which can provide insights into the mechanisms underlying human speech perception.

Stats

The dataset used in this study is the English Consistent Confusion Corpus (ECCC), which contains over 3,000 consistent misperceptions of common English words mixed with different types of noise maskers (stationary speech-shaped noise, four-speaker babble, and three-speaker babble modulated noise).

Quotes

"Our method outperformed the considered baselines, even in a zero-shot setup, and yields a relative improvement of up to 66% when fine-tuned to predict listeners' responses."
"Interestingly, we observed that fine-tuning to predict the full distribution of listeners' responses results in overall better performance than training to predict the mode of the distribution, even if one is only interested on predicting the mode."
"We obtained the most performance gains from fine-tuning the convolutional encoder, which suggests that the largest difference between Whisper's and human speech processing is at the low acoustic level."

Key Insights Distilled From

Transfer Learning from Whisper for Microscopic Intelligibility Prediction

by Paul Best,Sa... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01737.pdf

Deeper Inquiries

How could the performance of the microscopic intelligibility prediction model be further improved by incorporating additional information, such as speaker characteristics or contextual cues

To enhance the performance of the microscopic intelligibility prediction model, incorporating additional information such as speaker characteristics and contextual cues could be beneficial. Speaker characteristics like gender, age, or accent can influence speech perception and intelligibility. By including this information in the model, it can learn to adapt its predictions based on specific speaker attributes, potentially improving accuracy. Contextual cues, such as the topic of conversation or the emotional state of the speaker, can also play a role in how speech is perceived. Integrating these cues into the model can provide a more comprehensive understanding of the speech context, leading to more accurate predictions of listener responses. By leveraging speaker characteristics and contextual cues, the model can capture the nuances of human speech perception more effectively, thereby enhancing its overall performance in microscopic intelligibility prediction tasks.

What are the potential implications of the model's struggles with four-speaker babble noise, and how could this be addressed to better capture the complex effects of different noise types on human speech perception

The model's struggles with four-speaker babble noise, as observed in the study, highlight the complex effects of different noise types on human speech perception. Four-speaker babble noise introduces additional challenges due to the overlapping voices, making it harder for both humans and models to decipher speech accurately. To address this issue and better capture the effects of various noise types, several strategies can be employed. One approach could involve training the model on a more diverse set of noise types, including variations of multi-speaker babble noise. This exposure can help the model learn to differentiate between different noise profiles and adapt its predictions accordingly. Additionally, incorporating noise-specific features or preprocessing techniques tailored to handle complex noise environments like four-speaker babble can improve the model's robustness. By fine-tuning the model on a wider range of noise scenarios and optimizing it to handle specific challenges posed by four-speaker babble noise, the model can better simulate human speech perception in real-world noisy conditions.

Could the insights gained from this work on microscopic intelligibility prediction be leveraged to improve the robustness and accuracy of automatic speech recognition systems, particularly in challenging noisy environments

The insights gained from this work on microscopic intelligibility prediction can indeed be leveraged to enhance the robustness and accuracy of automatic speech recognition (ASR) systems, especially in challenging noisy environments. By understanding how humans perceive speech in noisy conditions at a detailed level, ASR systems can be optimized to mimic human-like perception and improve their performance. The transfer learning techniques and fine-tuning strategies employed in microscopic intelligibility prediction can be applied to ASR models to make them more resilient to noise and better at recognizing speech accurately. By training ASR systems on datasets that include noise-induced word misperceptions and leveraging the knowledge gained from microscopic intelligibility modeling, these systems can learn to adapt to noisy environments and produce more reliable transcriptions. Additionally, incorporating insights on how different noise types affect human speech perception can help ASR systems differentiate between various noise profiles and adjust their processing accordingly, leading to more robust and accurate speech recognition in challenging acoustic conditions.