Joint Speech and Text Training for Decoder-Only ASR Using a CTC Compressor: An Analysis of CJST

Основні поняття

CJST, a novel framework for decoder-only automatic speech recognition (ASR), leverages a CTC compressor to effectively integrate speech and text data during training, leading to improved performance in both in-domain and cross-domain scenarios.

Анотація

Bibliographic Information:

Zhou, W., Jia, J., Sari, L., Mahadeokar, J., & Kalinli, O. (2024). CJST: CTC Compressor based Joint Speech and Text Training for Decoder-Only ASR. arXiv preprint arXiv:2411.07607.

Research Objective:

This paper introduces CJST, a novel framework for improving decoder-only Automatic Speech Recognition (ASR) by leveraging a CTC compressor for joint speech and text training. The study aims to enhance ASR performance, particularly in scenarios where external language models are not used.

Methodology:

The researchers developed CJST, which utilizes a CTC compressor to align speech and text representations. They explored various compression modes, edge case handling techniques, and the impact of embedding sharing. The framework was evaluated on the Librispeech and in-house datasets, comparing its performance against traditional adaptor-based methods. Additionally, the effectiveness of joint speech and text training was assessed in both in-domain and cross-domain scenarios using Librispeech and TED-LIUM2 datasets.

Key Findings:

• The study found that while CTC compressor-based approaches can be sensitive to compression modes, employing blank probability removal with a high threshold consistently yielded the best results for both clean and noisy data.
• CJST effectively facilitated joint speech and text training by aligning speech and text modalities, leading to improved performance compared to traditional adaptor-based methods and internal language model training.
• Experiments on Librispeech and TED-LIUM2 datasets demonstrated that CJST achieved superior performance in both in-domain and cross-domain text injection scenarios.

Main Conclusions:

The authors conclude that CJST offers a robust and effective approach for joint speech and text training in decoder-only ASR systems. The framework's ability to leverage the CTC compressor for modality alignment and its strong performance in various scenarios highlight its potential for advancing ASR technology.

Significance:

This research significantly contributes to the field of ASR by introducing a novel framework for joint speech and text training in decoder-only models. The findings have practical implications for developing more accurate and efficient ASR systems, particularly in scenarios where external language models are limited or unavailable.

Limitations and Future Research:

The study primarily focused on offline ASR tasks. Further research could explore the applicability and effectiveness of CJST in online or streaming ASR scenarios. Additionally, investigating the impact of different modality adaptor architectures and training strategies within the CJST framework could lead to further performance improvements.

Статистика

The proposed CJST framework achieves a 6% relative improvement on cross-domain evaluation and a small further improvement on in-domain evaluation.
The most robust compression mode for the CTC compressor is blank probability removal with a high threshold (0.95).
Using the empty-fallback scheme for handling empty outputs from the CTC compressor is a better choice than the empty-skip scheme.
Sharing CTC and text embeddings does not show consistent advantages.

Цитати

Ключові висновки, отримані з

CJST: CTC Compressor based Joint Speech and Text Training for Decoder-Only ASR

by Wei Zhou, Ju... о arxiv.org 11-13-2024

https://arxiv.org/pdf/2411.07607.pdf

Глибші Запити

How might the CJST framework be adapted for low-resource languages where large amounts of paired speech-text data are scarce?

Adapting CJST for low-resource languages with limited paired speech-text data presents a challenge, but several strategies can be employed:

Leveraging Cross-lingual Transfer Learning: Pre-training the audio encoder on a high-resource language and then fine-tuning it on the low-resource language can be beneficial. This allows the model to learn general acoustic representations that can be transferred. Similarly, initializing the decoder with a pre-trained language model from a related high-resource language can be helpful.
Data Augmentation: Techniques like speed perturbation, pitch shifting, and adding noise can artificially increase the size and diversity of the training data, improving the model's robustness. Back-translation of text data from a high-resource language can also augment the text-only training data.
Semi-Supervised Learning: Utilizing unpaired speech and text data can be valuable. For instance, the audio encoder can be trained on unpaired speech data with a CTC loss, while the decoder can be trained on unpaired text data as a language model.
Multilingual and Cross-lingual Training: Training a single model on multiple languages, including the low-resource language, can be advantageous. This allows the model to share knowledge across languages, potentially improving performance on the low-resource language.
Using Smaller Models: In low-resource settings, smaller models with fewer parameters might generalize better and be less prone to overfitting. Techniques like model pruning or knowledge distillation can be used to reduce the size of pre-trained models while retaining performance.
By combining these approaches, it's possible to adapt CJST for low-resource languages and improve ASR performance even with limited paired data.

Could the reliance on the CTC compressor potentially limit the model's ability to generalize to unseen acoustic conditions or speaking styles?

Yes, the reliance on the CTC compressor in the CJST framework could potentially limit the model's ability to generalize to unseen acoustic conditions or speaking styles. Here's why:

Dependence on CTC Probabilities: The CTC compressor heavily relies on the quality of the CTC probabilities generated by the audio encoder. If the encoder is trained on data with limited acoustic variability, the CTC probabilities might not accurately reflect the underlying phoneme sequence for unseen conditions, leading to suboptimal compression and impacting the decoder's performance.
Forced Peaky Alignment: The forced peaky alignment used in CJST during training might create a bias towards the specific alignment patterns observed in the training data. This could make the model less flexible in handling variations in pronunciation, rhythm, and prosody encountered in unseen acoustic conditions or speaking styles.
Overfitting to Training Data Characteristics: If the training data primarily consists of speech with specific acoustic characteristics, the CTC compressor might overfit to these characteristics. This could lead to poor generalization to speech with different background noise, reverberation, or speaker accents.
To mitigate these potential limitations, it's crucial to:

Ensure Acoustic Diversity in Training Data: Training the audio encoder on a diverse dataset encompassing a wide range of acoustic conditions, speaker accents, and speaking styles is essential.
Explore Alternative Compression Mechanisms: Investigating alternative compression mechanisms that are less sensitive to variations in CTC probabilities and alignment patterns could be beneficial.
Regularization and Data Augmentation: Employing regularization techniques during training and augmenting the data with various acoustic conditions can improve the model's robustness and generalization ability.
Addressing these points can help ensure that the CJST framework remains effective even when faced with unseen acoustic conditions and speaking styles.

What are the ethical implications of developing increasingly accurate and human-like ASR systems, and how can these concerns be addressed responsibly?

Developing increasingly accurate and human-like ASR systems presents several ethical implications that need careful consideration:

Job Displacement: As ASR systems become more sophisticated, they could potentially automate jobs currently performed by humans, such as transcription, customer service, and data entry, leading to unemployment.
Privacy Violation: Highly accurate ASR systems could be used for covert surveillance and unauthorized recording of conversations, infringing on individuals' privacy and raising concerns about data security.
Bias and Discrimination: If ASR systems are trained on biased data, they can perpetuate and even amplify existing societal biases related to gender, race, accent, or dialect, leading to unfair or discriminatory outcomes.
Misinformation and Manipulation: Realistic and human-like ASR systems could be used to create deepfakes or synthetic audio recordings, potentially spreading misinformation, manipulating public opinion, or causing harm to individuals.
Over-reliance and Loss of Human Skills: Excessive reliance on ASR systems might lead to a decline in human listening and communication skills, potentially impacting interpersonal interactions and social dynamics.
To address these concerns responsibly, developers and researchers should prioritize:

Transparency and Explainability: Making ASR systems more transparent and explainable can help identify and mitigate biases, build trust with users, and ensure accountability.
Data Privacy and Security: Implementing robust data privacy and security measures is crucial to prevent unauthorized access, use, or disclosure of sensitive audio data.
Bias Detection and Mitigation: Developing techniques to detect and mitigate biases in training data and model outputs is essential to ensure fairness and prevent discrimination.
User Education and Awareness: Educating users about the capabilities and limitations of ASR systems, as well as the potential ethical implications, is crucial to promote responsible use.
Regulation and Policy Development: Establishing clear guidelines, regulations, and policies governing the development, deployment, and use of ASR systems is essential to mitigate potential risks and ensure ethical considerations are addressed.
By proactively addressing these ethical implications, we can harness the benefits of advanced ASR technology while mitigating potential harms and ensuring its responsible development and deployment.