Latent Speech-Text Transformer
Abstract
Auto-regressive speech-text models are typically pre-trained on a large number of interleaved sequences of text tokens and raw speech encoded as speech tokens using vector quantization. These models have demonstrated state-of-the-art performance in speech-to-speech understanding and generation benchmarks, together with promising scaling laws, primarily enabled by the representational alignment between text and speech. Nevertheless, they suffer from shortcomings, partly owing to the disproportionately longer sequences of speech tokens in contrast to textual tokens. This results in a large compute imbalance between modalities during pre-training as well as during inference, and a potential hindrance to effectively aligning speech and text, ultimately translating to several orders of magnitude slower scaling laws. We introduce the Latent Speech-Text Transformer (LST), which makes pre-training speech-text models more data-efficient by dynamically and inexpensively aggregating speech tokens into latent speech patches. These patches serve as higher-level units that can either align with corresponding textual units to aid capability transfer or even encapsulate common speech sequences like silences to be more compute-efficient. We show that LST outperforms vanilla approaches on speech-to-speech as well as text-to-text benchmarks in both data- and compute-controlled settings, the former indicating more effective representational alignment and the latter indicating steeper scaling laws for speech-text models. On HellaSwag story completion, LST achieves 6.5% absolute gain in speech accuracy under compute-controlled training and 5.3% under data-controlled training, while also improving text performance. We will release our models, code, and the evaluation data to facilitate further research.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The Latent Speech-Text Transformer (LST) presents a significant advancement in speech-text modeling by improving efficiency and representational alignment between modalities. The comprehensive methodology and robust experimental results position this work as a valuable contribution to the field of machine learning, particularly in multimodal applications.
Methodology
The Latent Speech-Text Transformer (LST) introduces a novel approach to speech-text modeling by dynamically aggregating speech tokens into higher-level latent patches, which improves efficiency and alignment between modalities. The methodology is well-structured, leveraging existing architectures like the byte-latent transformer while innovating through curriculum patching and alignment strategies. The paper provides a thorough explanation of the local and global transformer components, detailing how they interact to enhance representational alignment and computational efficiency. However, while the methods are innovative, they heavily rely on existing frameworks and techniques, which slightly diminishes the perceived novelty.
Results & evaluation
The experimental evaluation is robust, utilizing multiple benchmarks (HellaSwag, StoryCloze, TopicStoryCloze) to demonstrate the effectiveness of LST in both speech-to-speech and text-to-text tasks. The results show consistent improvements over baseline models, particularly in compute-controlled and data-controlled settings. The ablation studies on different patching strategies provide valuable insights into the effectiveness of the proposed methods. However, the paper could benefit from a more extensive discussion on the statistical significance of the results and comparisons with more recent models.
Reproducibility
The paper includes sufficient details regarding the training setup, datasets, and model architecture, which aids in reproducibility. However, the absence of a publicly available code repository or demo URL limits the practical reproducibility of the results. The authors mention plans to release models and code, which is a positive step towards enhancing reproducibility in future work.
Limitations
The paper acknowledges several limitations, including the focus on half-duplex speech-text modeling and reliance on forced alignments during pre-training. It also does not explore instruction fine-tuning or downstream adaptations, which could be crucial for real-world applications. Additionally, the potential for errors introduced by auxiliary models during inference is noted, indicating areas for improvement.
Broader impact
The proposed LST model has significant implications for improving speech understanding and generation, particularly in applications requiring efficient multimodal processing. By addressing the imbalance between speech and text modalities, the model could enhance accessibility features in technology, such as real-time translation and voice recognition systems. However, as with all large language models, there are inherent risks of misuse and ethical considerations that need to be addressed. The Latent Speech-Text Transformer (LST) presents a significant advancement in speech-text modeling by improving efficiency and representational alignment between modalities. The comprehensive methodology and robust experimental results position this work as a valuable contribution to the field of machine learning, particularly in multimodal applications.
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