Leveraging Acoustic and Linguistic Characteristics for Semi-Supervised Cognitive State Classification from Speech

Conceitos Básicos

A novel semi-supervised learning framework that integrates multi-view pseudo-labeling to leverage both acoustic and linguistic characteristics for efficient cognitive state classification from speech.

Resumo

The proposed semi-supervised learning (SSL) framework introduces a multi-view pseudo-labeling method that utilizes both acoustic and linguistic characteristics to select the most confident unlabeled data for training a bimodal classifier.

The acoustic path employs Fréchet Audio Distance (FAD) to measure the similarity between labeled and unlabeled data based on embeddings from multiple audio encoders. The linguistic path uses large language models (LLMs) with task-specific prompts to predict labels from automatic speech recognition (ASR) transcriptions, leveraging insights from acoustics, linguistics, and psychology.

Data with matching pseudo-labels from both paths are considered high-confidence and used to train the initial bimodal classifier. The classifier is then iteratively updated by incorporating low-confidence data whose pseudo-labels align with either the acoustic or linguistic path. Multiple fusion techniques are compared to effectively utilize the multi-view knowledge.

The proposed SSL framework is evaluated on emotion recognition and dementia detection tasks, demonstrating competitive performance compared to fully supervised learning while using only 30% of the labeled data. It also significantly outperforms selected baselines, including decision merging and co-training.

Estatísticas

The lack of labeled data is a common challenge in speech classification tasks, particularly those requiring extensive subjective assessment.
Emotion recognition and dementia detection are crucial speech classification tasks with applications in human-computer interaction, health monitoring, and clinical diagnosis.
The IEMOCAP dataset contains 5,500 utterances with 4 emotion classes, and the ADReSSo dataset contains 2,268 utterances with 2 cognitive state classes (Alzheimer's Dementia and Cognitively Normal).

Citações

"The lack of labeled data is a common challenge in speech classification tasks, particularly those requiring extensive subjective assessment."
"We propose a novel SSL framework, integrating multi-view pseudo-labeling that leverages both acoustic and linguistic characteristics to select the most confident data for model training."
"Our proposed SSL framework is evaluated on emotion recognition and dementia detection tasks, demonstrating competitive performance using only 30% of the labeled data compared to fully supervised learning, and showing greater effectiveness than the selected baselines."

Principais Insights Extraídos De

Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling

by Yuanchao Li,... às arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16937.pdf

Perguntas Mais Profundas

How can the proposed multi-view pseudo-labeling approach be extended to other speech classification tasks beyond emotion recognition and dementia detection?

The proposed multi-view pseudo-labeling approach can be effectively extended to various speech classification tasks by adapting the framework to leverage the unique characteristics of different domains. For instance, in tasks such as speaker identification, sentiment analysis, or even medical diagnosis from speech, the multi-view pseudo-labeling can utilize both acoustic and linguistic features tailored to the specific requirements of these tasks.

Task-Specific Feature Extraction: Different speech classification tasks may benefit from specialized feature extraction methods. For example, in speaker identification, the acoustic path could focus on speaker-specific voice characteristics, while the linguistic path could analyze the content of speech for contextual clues.

Diverse Audio Encoders: The framework can incorporate additional audio encoders that are optimized for the specific characteristics of the new tasks. For instance, using encoders trained on speaker recognition datasets can enhance the acoustic path for speaker identification tasks.

Linguistic Model Adaptation: The linguistic path can be adapted by fine-tuning large language models (LLMs) on domain-specific corpora. For instance, in medical diagnosis, LLMs can be trained on clinical notes or patient interactions to improve the accuracy of pseudo-labeling based on ASR transcriptions.

Integration of Additional Modalities: The framework can be expanded to include other modalities, such as visual data from lip movements or facial expressions, which can complement the acoustic and linguistic features, especially in tasks like sentiment analysis or affective computing.

Iterative Refinement: The iterative nature of the semi-supervised training can be maintained, allowing the model to continuously improve its performance as more unlabeled data becomes available, thus enhancing its applicability across various speech classification tasks.

By leveraging these strategies, the multi-view pseudo-labeling approach can be adapted to a wide range of speech classification tasks, enhancing its versatility and effectiveness.

What are the potential limitations of relying on ASR transcriptions for linguistic pseudo-labeling, and how can the framework be further improved to address these limitations?

Relying on Automatic Speech Recognition (ASR) transcriptions for linguistic pseudo-labeling presents several potential limitations:

Error-Prone Transcriptions: ASR systems often produce transcriptions with errors, especially in noisy environments or with diverse accents. These errors can lead to incorrect pseudo-labels, negatively impacting the model's performance.

Lack of Contextual Understanding: ASR systems may struggle to capture the nuances of speech, such as sarcasm, idiomatic expressions, or emotional undertones, which are crucial for accurate classification in tasks like emotion recognition.

Limited Vocabulary: ASR systems may have a restricted vocabulary, leading to misrecognition of domain-specific terms or jargon, which can further degrade the quality of the transcriptions.

To address these limitations, the framework can be improved in several ways:

Enhanced ASR Models: Utilizing more advanced ASR models that are fine-tuned on specific datasets relevant to the task can improve transcription accuracy. Incorporating models that leverage contextual information can also help in reducing errors.

Error Correction Mechanisms: Implementing robust error correction mechanisms, such as the REVISE-REASON-RECOGNIZE (R3) pipeline, can help in refining ASR outputs before they are used for pseudo-labeling. This can involve using LLMs to correct common ASR errors based on context.

Multi-Model ASR Approaches: Employing an ensemble of multiple ASR models can provide a more reliable transcription by averaging out individual model errors. This can be particularly effective in generating a more accurate N-best hypothesis list for the R3 pipeline.

Feedback Loops: Establishing feedback loops where the model's predictions are used to iteratively refine the ASR outputs can enhance the overall quality of the linguistic pseudo-labels.

Incorporating Human-in-the-Loop: In critical applications, integrating human feedback into the labeling process can help in correcting errors and improving the quality of the training data.

By addressing these limitations, the framework can enhance the reliability of linguistic pseudo-labeling, leading to improved performance in speech classification tasks.

How can the semi-supervised training process be optimized to reduce the number of iterations required for convergence while maintaining high performance?

Optimizing the semi-supervised training process to reduce the number of iterations required for convergence while maintaining high performance can be achieved through several strategies:

Adaptive Learning Rates: Implementing adaptive learning rate techniques, such as learning rate scheduling or using optimizers like Adam or RMSprop, can help the model converge faster by adjusting the learning rate based on the training progress.

Early Stopping Criteria: Establishing more stringent early stopping criteria based on validation performance can prevent unnecessary iterations. Monitoring metrics such as validation loss or accuracy can help in determining when to halt training.

Data Selection Strategies: Enhancing the data selection process for high-confidence pseudo-labels can lead to more effective training. By employing uncertainty sampling or leveraging confidence scores from the bimodal classifier, the model can focus on the most informative samples, reducing the number of iterations needed for convergence.

Batch Size Optimization: Experimenting with different batch sizes can impact convergence speed. Larger batch sizes can lead to faster convergence but may require more memory, while smaller batch sizes can provide more frequent updates but may slow down the process. Finding an optimal balance is crucial.

Regularization Techniques: Incorporating regularization techniques, such as dropout or weight decay, can help prevent overfitting and improve generalization, allowing the model to converge more effectively with fewer iterations.

Ensemble Methods: Utilizing ensemble methods where multiple models are trained simultaneously can provide a more robust prediction, allowing for faster convergence as the models can learn from each other's strengths.

Transfer Learning: Leveraging pre-trained models and fine-tuning them on the specific task can significantly reduce the number of iterations required for convergence, as the model starts from a more informed state.

By implementing these strategies, the semi-supervised training process can be optimized, leading to faster convergence while maintaining or even enhancing the model's performance in speech classification tasks.