Latent Speech-Text Transformer

ICLR 2026 Conference SubmissionAnonymous Authors

OpenReview Score: 6.0 Download Report PDF

Speech–Text ModelsLatent PatchingMultimodal AlignmentLarge Language Models

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.

Abstract:

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.

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Overview

Overall Novelty Assessment

The paper introduces the Latent Speech-Text Transformer (LST), which dynamically aggregates speech tokens into latent patches to improve alignment with text units during pre-training. According to the taxonomy, this work occupies the 'Latent Speech Patch Aggregation' leaf under 'Speech-Text Unified Modeling', where it is currently the sole representative among fourteen total papers across the field. This placement suggests the paper explores a relatively sparse research direction within the broader landscape of efficient speech-text modeling, distinguishing itself from sibling approaches that employ multi-task decoder-only or encoder-decoder architectures without dynamic patch-level aggregation.

The taxonomy reveals neighboring work in 'Multi-Task Decoder-Only Speech-Text Models' and 'Encoder-Decoder Speech-Text Architectures', which share the goal of unified speech-text processing but differ in architectural strategy. Adjacent branches include 'Speech Tokenization Frameworks' focusing on discrete representations and 'Token Compression Techniques' addressing inference-time reduction. The LST approach appears to bridge these areas by embedding compression dynamics directly into the pre-training architecture rather than treating tokenization and compression as separate stages, thereby diverging from methods that apply fixed-rate downsampling or post-hoc merging strategies.

Among twenty-one candidates examined, none clearly refute the three core contributions: the LST architecture itself (one candidate examined), multiple patching strategies including curriculum patching (ten candidates), and demonstrated performance improvements (ten candidates). The limited search scope means this analysis captures top semantic matches and immediate citations but does not constitute an exhaustive field survey. The absence of refutable prior work across all contributions suggests that, within this bounded search, the combination of dynamic patch aggregation, curriculum-based training, and empirical validation appears relatively underexplored, though broader literature may contain relevant precedents not surfaced here.

Based on the top-twenty-one semantic matches, the work appears to occupy a distinct niche by integrating patch-level dynamics into pre-training rather than relying solely on inference-time compression or static tokenization. However, the analysis does not cover all possible related work in speech representation learning, multimodal alignment, or hierarchical tokenization, leaving open the possibility that similar aggregation strategies exist in adjacent domains or under different terminological framing.

Taxonomy

Core-task Taxonomy Papers

Claimed Contributions

Contribution Candidate Papers Compared

Refutable Paper

Research Landscape Overview

Core task: Efficient speech-text modeling through dynamic token aggregation. The field addresses the computational challenge of jointly processing speech and text in unified models, where speech representations typically produce far more tokens than their textual counterparts. The taxonomy reveals four main branches: Speech-Text Unified Modeling explores architectures that handle both modalities within a single framework, often employing latent representations or cross-modal alignment strategies (e.g., VoxtLM[4], OmniDRCA[1]); Speech Tokenization Frameworks focus on discrete or continuous representations that convert raw audio into manageable units; Token Compression Techniques develop methods to reduce sequence length through merging, pruning, or hierarchical aggregation (e.g., QuickMerge[7], Neural Token Compression[5]); and Adaptive Training Techniques investigate dynamic strategies that adjust compression or attention patterns during learning (e.g., TempMe[3], TASLA[6]). These branches are interconnected, as effective unified modeling often relies on both robust tokenization and intelligent compression to balance expressiveness with efficiency. Recent work has intensified around adaptive and learnable compression schemes that go beyond fixed-rate downsampling. Several studies explore entropy-driven or semantic-aware merging (Entropy Semantic Speech[8], OmniZip[2]) to preserve critical information while aggressively reducing token counts, and others investigate prompt-based or multimodal fusion strategies (Multimodal Promptable Merging[12], DyMU[13]) to handle varying input characteristics. Within this landscape, Latent Speech-Text Transformer[0] sits in the Latent Speech Patch Aggregation cluster, emphasizing dynamic aggregation of speech patches in a shared latent space. Compared to approaches like TempMe[3] or Neural Token Compression[5], which may apply more uniform or layer-wise compression, Latent Speech-Text Transformer[0] appears to focus on patch-level grouping that adapts to local speech structure, aiming to tighten the gap between speech and text token densities without sacrificing cross-modal alignment. This positions it as a representative example of unified modeling that integrates tokenization and compression within a single learned framework.

Claimed Contributions

Latent Speech-Text Transformer (LST) architecture

1 retrieved paper

The authors propose LST, an architecture that aggregates speech tokens into higher-level latent patches using a local encoder, processes these patches with a global transformer alongside text tokens, and decodes them back to speech tokens. This approach addresses the compute imbalance between speech and text modalities and improves representational alignment.

1 retrieved paper

Multiple speech patching strategies including curriculum patching

10 retrieved papers

The authors develop several patching methods: static patching with fixed-length segments, alignment-based patching using forced alignment timestamps, mixed patching combining both approaches, and curriculum patching that transitions from aligned to static patching during training to eliminate alignment dependency at inference.

10 retrieved papers

Demonstration of improved performance and scalability

10 retrieved papers

The authors demonstrate that LST outperforms baseline speech-text models on benchmarks like HellaSwag, StoryCloze, and TopicStoryCloze in both compute-controlled and data-controlled experimental settings, achieving gains in both speech-to-speech and text-to-text tasks while maintaining scalability from 1B to 7B parameters.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution