Automated Assessment of Speech Intelligibility and Severity in Patients with Head and Neck Cancers using ASR-Powered Wav2Vec2

Core Concepts

The core message of this paper is that using a Wav2Vec2 model pre-trained on automatic speech recognition (ASR) tasks can outperform models pre-trained on self-supervised learning (SSL) tasks for the assessment of speech intelligibility and severity in patients with head and neck cancers, even with limited training data.

Abstract

This paper explores the use of pre-trained Wav2Vec2 models for the task of assessing speech intelligibility and severity in patients with head and neck cancers. The authors compare the performance of Wav2Vec2 models pre-trained on self-supervised learning (SSL) tasks versus those pre-trained on ASR tasks.
The key highlights are:

The authors propose training the model on the entire audio file rather than segmenting it, to preserve important context and speech information.
They find that the Wav2Vec2 model pre-trained on ASR tasks outperforms the SSL-trained model, achieving an average MSE of 0.73 for intelligibility prediction and 1.15 for severity prediction, using only 95 training samples.
This ASR-based approach establishes a new baseline, outperforming previous state-of-the-art systems by a significant margin.
Further analysis shows the model's ability to generalize across different speech content and its robustness to varying segment durations.
The strong correlation between ASR performance and speech quality assessment suggests a deep connection between the two tasks.

Stats

"The system achieved an average best MSE at 0.73 for the intelligibility prediction task and 1.15 for the severity prediction task."
"The proposed architectures achieved outstanding results without requiring data augmentation."

Quotes

"Remarkably, most of the systems we proposed consistently outperform these existing baseline systems. With our best model, we achieved between 58% to 75% MSE reduction compared with the two baselines reported above for intelligibility assessment and between 40% to 62% to MSE reduction for severity assessment within the context of SpeeCOmco corpus."
"While comparing feature extractor based on pre-trained SSL with pre-trained ASR, it is surprising that pre-trained ASR extractor outperformed the pre-trained SSL one. Not only with better average MSE, pre-trained ASR extractor also shows a more consistent performance with a significantly smaller standard deviation."

Key Insights Distilled From

Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context

by Tuan Nguyen,... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20184.pdf

Deeper Inquiries

How can the insights from this work be applied to improve speech assessment systems for other types of speech disorders, such as those caused by Parkinson's disease or other neurological conditions?

The insights gained from this study can be applied to enhance speech assessment systems for various speech disorders, including those caused by Parkinson's disease or other neurological conditions. By utilizing ASR-powered Wav2Vec2 models as feature extractors, similar to the approach outlined in the research, it is possible to develop more accurate and efficient assessment systems. These models can be fine-tuned on specific datasets related to Parkinson's disease or other neurological conditions to capture the unique speech characteristics associated with these disorders. By training the models at the audio level without data augmentation, as proposed in the study, the systems can learn to provide assessments based on the entire speech signal, considering the context and content of the speech. This approach can lead to more reliable and consistent evaluations of speech quality for individuals with different types of speech disorders.

What other speech-related tasks, beyond intelligibility and severity assessment, could benefit from the use of ASR-powered Wav2Vec2 models as feature extractors?

Beyond intelligibility and severity assessment, ASR-powered Wav2Vec2 models can be beneficial for various other speech-related tasks. Some of these tasks include:

Emotion Recognition: By extracting features from speech signals using ASR-powered models, it is possible to analyze prosodic features and acoustic cues that convey emotions in speech. This can aid in developing emotion recognition systems for applications in affective computing and human-computer interaction.

Speaker Diarization: Wav2Vec2 models can assist in speaker diarization tasks by extracting speaker embeddings and segmenting speech signals based on speaker identities. This can be valuable in scenarios such as call center analytics, meeting transcription, and forensic analysis.

Speech Transcription: ASR-powered models can be utilized for accurate and efficient speech-to-text transcription tasks. By leveraging the feature extraction capabilities of Wav2Vec2, transcription systems can achieve higher accuracy and robustness in converting spoken language into text.

Accent Recognition: Wav2Vec2 models can be employed to identify and analyze different accents in speech signals. This can be useful in language learning applications, speech dialectology studies, and improving the performance of accent-specific ASR systems.

Speech Enhancement: ASR-powered models can be utilized for noise reduction and speech enhancement tasks by extracting relevant features from noisy speech signals. This can improve the overall quality and intelligibility of speech in noisy environments.

Could the findings from this study inform the development of more interpretable and explainable speech assessment models, potentially by leveraging the insights gained from the ASR-based approach?

The findings from this study can indeed contribute to the development of more interpretable and explainable speech assessment models by leveraging the insights gained from the ASR-based approach. By using ASR-powered Wav2Vec2 models as feature extractors, the models can capture meaningful representations from speech signals, enabling a deeper understanding of the underlying features that contribute to speech quality assessment.
To enhance interpretability and explainability, researchers can explore techniques such as attention mechanisms to visualize the model's focus on specific parts of the speech signal during assessment. By analyzing the attention weights, it becomes possible to explain why certain predictions are made and provide insights into the decision-making process of the model. Additionally, feature importance analysis can be conducted to identify the most influential features extracted by the model in determining speech quality scores.
Furthermore, the use of ASR-based approaches can facilitate the integration of linguistic content analysis into speech assessment models, enabling a more comprehensive evaluation of speech quality. By incorporating linguistic features extracted by ASR models, the assessment systems can provide explanations based on linguistic patterns, phonetic characteristics, and language-specific nuances present in the speech signal. This holistic approach can lead to more transparent and interpretable speech assessment models, benefiting both clinicians and researchers in understanding and interpreting the assessment results.