Enhancing Automatic Speech Understanding through Text-to-Speech Imputation for Robust Performance in Missing Speech Modality Scenarios

核心概念

Leveraging text-to-speech imputation to enhance automatic speech understanding performance in scenarios with missing speech modality during training or testing.

摘要

The paper proposes TI-ASU, a Text-to-speech Imputation approach for Automatic Speech Understanding (ASU), to address the challenge of missing speech modality in ASU applications. The core idea is to impute the missing speech modality using pre-trained text-to-speech (TTS) models.

The authors first investigate scenarios where speech data is missing in the training set, and show that TI-ASU can substantially improve ASU performance compared to training with limited real speech data. Even in extreme cases where 95% of speech data is missing, TI-ASU outperforms training with the remaining 5% real speech.

The authors further explore cases where speech data can be missing in both training and testing sets. They propose TI-ASU Dropout, which combines TI-ASU with dropout training to enhance model robustness against missing speech modality during inference. The results demonstrate that TI-ASU Dropout provides competitive or better performance than multimodal dropout training, especially when a significant portion of speech data is missing.

Additionally, the authors investigate leveraging large language models (LLMs) to augment the text transcriptions used for TTS-based speech imputation. While the LLM-assisted approach shows promise, the authors identify challenges in maintaining the quality of the generated speech samples.

Overall, the paper presents a comprehensive study on enhancing ASU through TTS-based speech imputation, demonstrating the effectiveness of the proposed TI-ASU framework in addressing missing speech modality challenges.

統計資料

"Automatic Speech Understanding (ASU) aims at human-like speech interpretation, providing nuanced intent, emotion, sentiment, and content understanding from speech and language (text) content conveyed in speech."
"To ensure that the ASU model reaches a fair convergence, an ideal corpus is expected to contain an adequate amount of complete audio samples that cover diverse speech attributes, such as emotion, intonation, and speaker demographics."
"Multiple practical constraints–both technological and human–hinge on the collection of the ASU datasets, such as hardware instability, imbalances in data resources, and the need for privacy protection."

引述

"To deal with limitations to access speech samples, it is hence necessary to modify the ASU toward robustness to 'missing' speech."
"The core idea of TI-ASU is to impute the missing speech modality with TTS models from text transcriptions."

從以下內容提煉的關鍵洞見

TI-ASU: Toward Robust Automatic Speech Understanding through Text-to-speech Imputation Against Missing Speech Modality

by Tiantian Fen... 於 arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17983.pdf

深入探究

How can the quality of the text-to-speech generated speech samples be further improved to enhance the performance of TI-ASU?

To enhance the quality of the text-to-speech (TTS) generated speech samples in the context of TI-ASU, several strategies can be implemented:

Fine-tuning TTS Models: Fine-tuning the pre-trained TTS models on domain-specific data can help improve the quality of the generated speech. By training the TTS models on a more extensive and diverse dataset that closely resembles the target domain, the generated speech samples are likely to be more accurate and natural.

Data Augmentation: Increasing the diversity of the training data used for TTS models can lead to more robust and varied speech generation. Techniques such as adding noise, varying pitch, speed, and accent in the training data can help the TTS models learn to generate speech samples that are more realistic and expressive.

Multi-TTS Ensemble: Instead of relying on a single TTS model, employing an ensemble of multiple TTS models can enhance the diversity and quality of the generated speech. By combining the outputs of different TTS models, TI-ASU can benefit from a broader range of speech styles and nuances.

Prompt Design: Designing more effective prompts for the TTS models can guide the generation process towards producing higher-quality speech samples. Crafting prompts that capture the desired tone, emotion, and style of speech can result in more accurate and contextually relevant speech generation.

Post-Processing Techniques: Applying post-processing techniques such as signal processing algorithms or voice enhancement tools can further refine the generated speech samples. These techniques can help reduce noise, improve clarity, and enhance the overall quality of the speech output.

By implementing these strategies, the quality of the text-to-speech generated speech samples can be enhanced, leading to improved performance of TI-ASU in automatic speech understanding tasks.

How can the proposed TI-ASU framework be extended to address missing modalities in other multimodal learning tasks beyond automatic speech understanding?

The TI-ASU framework can be extended to address missing modalities in various other multimodal learning tasks by adapting the core principles and methodologies to suit different domains. Here are some ways in which the TI-ASU framework can be applied to other multimodal learning tasks:

Image Recognition: In tasks involving image recognition with missing modalities, TI-ASU can be modified to impute missing visual data using image-to-text models. By leveraging pre-trained image recognition models and text generation techniques, missing visual information can be synthesized to enhance the performance of multimodal image recognition systems.

Video Analysis: For tasks related to video analysis where certain modalities are missing, TI-ASU can be extended to incorporate video-to-text models for imputing missing video content. By generating textual descriptions of the missing video segments, the framework can provide a comprehensive understanding of the multimodal data for improved video analysis.

Sensor Data Fusion: In scenarios involving sensor data fusion with missing modalities, TI-ASU can be adapted to integrate sensor-to-text models for imputing missing sensor readings. By converting sensor data into textual representations, the framework can facilitate the fusion of diverse sensor inputs and enhance the robustness of multimodal sensor data analysis.

Healthcare Applications: In healthcare applications where multimodal data from various sources is utilized, TI-ASU can be applied to address missing modalities in medical data. By incorporating medical imaging-to-text models and clinical text generation techniques, the framework can assist in imputing missing medical data modalities for more comprehensive healthcare analytics and decision-making.

By extending the TI-ASU framework to diverse multimodal learning tasks beyond automatic speech understanding, it can offer a versatile and adaptable solution for addressing missing modalities and enhancing the performance of multimodal learning systems across different domains.