Investigating Zero-Shot Generalizability on Mandarin-English Code-Switched ASR and Speech-to-text Translation of Recent Foundation Models with Self-Supervision and Weak Supervision (2401.00273v1)

Abstract: This work evaluated several cutting-edge large-scale foundation models based on self-supervision or weak supervision, including SeamlessM4T, SeamlessM4T v2, and Whisper-large-v3, on three code-switched corpora. We found that self-supervised models can achieve performances close to the supervised model, indicating the effectiveness of multilingual self-supervised pre-training. We also observed that these models still have room for improvement as they kept making similar mistakes and had unsatisfactory performances on modeling intra-sentential code-switching. In addition, the validity of several variants of Whisper was explored, and we concluded that they remained effective in a code-switching scenario, and similar techniques for self-supervised models are worth studying to boost the performance of code-switched tasks.

• The paper demonstrates that self-supervised techniques nearly match weakly supervised models in code-switched ASR and translation tasks.
• The paper reveals significant challenges with intra-sentential code-switching, highlighting difficulties in processing mixed-language dialogue.
• The paper identifies common error patterns and recommends multi-dimensional fine-tuning strategies to improve domain-specific term translation.

Evaluating Zero-Shot Generalizability and Supervision Techniques in Code-Switched ASR and ST Tasks

Introduction to Code-Switching Challenges

Code-switching (CS) presents a complex challenge in the field of automatic speech recognition (ASR) and speech-to-text translation (ST), particularly in a bilingual context like Mandarin-English. The phenomena occur naturally in multilingual societies, posing significant hurdles for speech processing technologies due to the intricate blending of languages. Despite the rapid advancement in ASR and ST methodologies, the nuanced dynamics of code-switching have not been sufficiently addressed, with most existing models requiring extensive labeled data for satisfactory performance. This paper shifts the focus towards evaluating the efficacy of large-scale models trained via self-supervision and weak supervision techniques in zero-shot CS scenarios, shedding light on potential avenues for improvement.

Models and Datasets Overview

The study rigorously tests a range of models, including SeamlessM4T, SeamlessM4T v2, and various iterations of Whisper, against three corpora specifically designed to benchmark code-switched ASR and ST performances. These models, renowned for their multilingual capabilities, undergo examination to ascertain their adaptability and effectiveness in recognizing and processing code-switched dialogue without explicit fine-tuning for such tasks.

Key Findings and Implications

The investigation yields several compelling insights:

• Performance Parity: Self-supervised models, notably SeamlessM4T v2, demonstrate a commendable proximity in performance to their weakly supervised counterparts. This suggests the considerable potential of self-supervised learning paradigms in contexts where labeled data is scarce, reinforcing the value of pre-training on diverse, unlabeled datasets.
• Challenges in Intra-sentential CS: Despite noteworthy achievements, all models exhibit pronounced difficulties in handling intra-sentential code-switching. This limitation underscores the necessity for models to develop a deeper understanding of the nuanced linguistic structures unique to code-switched speech.
• Error Patterns: Analytical observation of common error trends reveals models' tendencies to inaccurately translate or misinterpret domain-specific terminologies. These issues highlight critical areas for improvement in model training processes, emphasizing the need for a multi-dimensional approach to capturing the complexities of language.
• Efficacy of Whisper Variants: The exploration of Whisper variants, through techniques such as prompt-conditional fine-tuning and speech-based in-context learning, indicates significant promise in enhancing model performance on code-switched tasks. The results advocate for the exploration of similar strategies in self-supervised models to bolster their generalization capabilities.

Future Directions

The research highlights the paramount importance of advancing self-supervised and weakly supervised techniques tailored specifically for code-switching contexts. Innovations in model training methodologies, particularly those encouraging models to grasp the subtleties of intra-sentential code-switching, could dramatically enhance the robustness and applicability of speech technologies across diverse linguistic landscapes. Additionally, the paper suggests a fertile ground for future exploration in the development of models capable of synthesizing and applying world knowledge to better interpret and process speech across multiple languages and domains.

Conclusion

This study presents a crucial step forward in understanding the capacities and limitations of contemporary models in handling the complexities inherent in code-switched speech. By pinpointing specific shortcomings and highlighting the efficacy of certain supervisory techniques, this paper not only contributes valuable insights to the ongoing discourse on multilingual speech processing but also sets a promising trajectory for future research endeavored to bridge these gaps.