Pre-Trained Foundation Model representations to uncover Breathing patterns in Speech (2407.13035v1)

Abstract: The process of human speech production involves coordinated respiratory action to elicit acoustic speech signals. Typically, speech is produced when air is forced from the lungs and is modulated by the vocal tract, where such actions are interspersed by moments of breathing in air (inhalation) to refill the lungs again. Respiratory rate (RR) is a vital metric that is used to assess the overall health, fitness, and general well-being of an individual. Existing approaches to measure RR (number of breaths one takes in a minute) are performed using specialized equipment or training. Studies have demonstrated that machine learning algorithms can be used to estimate RR using bio-sensor signals as input. Speech-based estimation of RR can offer an effective approach to measure the vital metric without requiring any specialized equipment or sensors. This work investigates a machine learning based approach to estimate RR from speech segments obtained from subjects speaking to a close-talking microphone device. Data were collected from N=26 individuals, where the groundtruth RR was obtained through commercial grade chest-belts and then manually corrected for any errors. A convolutional long-short term memory network (Conv-LSTM) is proposed to estimate respiration time-series data from the speech signal. We demonstrate that the use of pre-trained representations obtained from a foundation model, such as Wav2Vec2, can be used to estimate respiration-time-series with low root-mean-squared error and high correlation coefficient, when compared with the baseline. The model-driven time series can be used to estimate $RR$ with a low mean absolute error (MAE) ~ 1.6 breaths/min.

• The paper introduces a novel Conv-LSTM methodology that leverages Wav2Vec2 representations to estimate respiration time-series from speech signals.
• It shows that pre-trained features outperform traditional mel-filterbank features, achieving a CCC of 0.77 with reduced RMSE and MAE.
• The approach offers practical implications for non-invasive health monitoring and can be integrated into consumer devices with close-talking microphones.

Pre-Trained Foundation Model Representations to Uncover Breathing Patterns in Speech

The paper "Pre-Trained Foundation Model Representations to Uncover Breathing Patterns in Speech" by Mitra et al. provides a rigorous examination of using pre-trained foundation models, specifically Wav2Vec2, to estimate respiration rates (RR) from speech signals recorded with close-talking microphones. The paper suggests a novel methodology that leverages ML models to derive respiratory metrics from speech, proposing potential impacts on health and wellness monitoring without the need for specialized equipment.

Key Contributions

1. Respiration Time-Series Estimation: The authors propose a convolutional long-short term memory network (Conv-LSTM) to estimate respiration time-series data from speech signals. The model is trained on a dataset of 26 subjects with groundtruth RR obtained from chest-belts.
2. Effectiveness of Pre-Trained Representations: Utilizing pre-trained representations from Wav2Vec2, the paper finds significant improvements in the estimation of RR compared to traditional mel-filterbank (MFB) features.
3. Saliency-Driven Feature Selection: The paper explores reducing model complexity through the selection of breath-relevant representations from the pre-trained Wav2Vec2 model, which helps in reducing model size without significant performance trade-offs.

Experimental Setup and Results

The authors collected speech data from 26 subjects, each recording sessions where groundtruth RR was measured using a commercial chest-belt. Conv-LSTM models were trained with both MFB features and Wav2Vec2 representations.

Key Metrics

• Concordance Correlation Coefficient (CCC): Measures the agreement between the estimated and the actual respiration time-series.
• Root Mean Squared Error (RMSE): Assesses the difference between the predicted and actual values.
• Mean Absolute Error (MAE): Specifically used to evaluate RR estimation accuracy.
• Accuracy at ±2 breaths/min (Acc@2bpm): Measures the segment-level accuracy within a predefined error tolerance.

Numerical Results

• The best performing representation, derived from layer 4 of the Wav2Vec2 model, achieved a CCC of 0.77, RMSE of 0.11, MAE of 1.6 breaths/min, and 84.4% Acc@2bpm on the test set. This demonstrates a marked improvement over traditional MFB features which had a CCC of 0.68 and RMSE of 0.13.

Implications and Future Directions

This paper suggests several practical and theoretical implications:

• Health Monitoring: The ability to estimate RR from speech has significant applications in non-invasive health monitoring, where traditional measurements could be cumbersome or intrusive.
• Integration in Consumer Devices: Given that close-talking microphones are ubiquitous in many consumer devices (e.g., smartphones, smart speakers), this approach could easily be scaled for widespread use.
• Model Robustness and Generalization: Future studies should validate the robustness of the proposed method on more diverse datasets, including conversational speech and other languages.

The paper also proposes directions for future research:

• Fine-Tuning: Investigating the impact of fine-tuning pre-trained models on respiration-relevant tasks to potentially improve performance.
• Larger Datasets: Utilizing larger and more diverse datasets to understand the model's generalization capabilities better.
• Real-Time Applications: Exploring the application of these models in real-time scenarios and their efficiency on edge devices.

Conclusion

Mitra et al. present a compelling case for the use of pre-trained foundation models in estimating respiratory metrics from speech signals, achieving high accuracy and low error rates. By leveraging sophisticated machine learning methodologies and representations from foundation models like Wav2Vec2, the research advances the potential for non-invasive health monitoring. Future work in this area promises to enhance the applicability and performance of such models across more varied and practical scenarios.