{"id":"b5ffb2af-d98b-469c-91e3-3d7c0c44a367","arxiv_id":"2506.01133","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Text models encode linguistic taxonomies early and densely; speech models develop them later and less prominently, with multimodal models showing intermediate patterns.","lead":"This study compares how speech, text, and multimodal AI models organize words into abstract concepts such as parts of speech and sentiment. It reports that text models build linguistic structure early and strongly, while speech models shift from acoustic to linguistic features later and more weakly.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Mean-pooling of frame embeddings may manufacture the layer-wise speech-to-linguistic transition and the apparent capacity gap.","rationale":"The reader's weakest assumption pointed to forced alignment and frame averaging; I agree that the aggregation pipeline is the fragile point, but I focus the concern more sharply on the averaging operator itself rather than MFA boundary accuracy. The central claim—that speech models transition from acoustic to linguistic representations and allocate less capacity to linguistic taxonomies—depends entirely on the layer-wise alignment curves in Fig. 2. Mean pooling is known to be a poor and lossy way to produce acoustic word embeddings; Sanabria et al. (2023), cited as [10], actually motivate learned pooling. Because early-layer frame representations in HuBERT are phone-sized and highly variable within a word, simple averaging can destroy word identity, artificially depressing alignment in lower layers. As layers become more word-uniform, the same averaging becomes more faithful, producing a monotonic rise that looks like a conceptual transition but may just be a property of the pooling operation. Similarly, the cross-modal comparison is asymmetric: BERT text embeddings are per-token contextual vectors, whereas speech word embeddings are sums/averages over variable numbers of frames, so any information loss from pooling systematically disadvantages speech models and can explain the 'less capacity' conclusion without invoking representational capacity. This concern is concrete and testable by varying the pooling method. The conditional verdict remains appropriate because the claim could be rescued by such a control, but without it the main conclusion is not yet established. I partially agree with the reader because they identified the same pipeline step, though they emphasized alignment accuracy rather than the pooling operator's depth-dependent bias.","tokens_in":7938,"tokens_out":6629,"duration_ms":78359,"concrete_test":"Recompute the Section 4.1 alignment curves (Fig. 2) for HuBERT and Seamless-Speech using an alternative word-level aggregation, such as a learned attention-pooling layer trained on a held-out word-classification task or the frame embedding at the word's final frame, while keeping all other settings identical. If the alignment curves still show low early-layer values and a late rise comparable to Fig. 2, the finding is robust to pooling; if the curves shift left or flatten, the central claim about gradual transition and reduced capacity is an artifact of mean pooling.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 2.1 derives word-level speech embeddings Ψw by averaging all frame embeddings within MFA word boundaries. The central finding in Fig. 2—that speech models show low linguistic alignment in early layers and a gradual rise toward upper layers—is interpreted as evidence that these models 'gradually transition from acoustic to linguistic representations.' This interpretation is not valid unless the pooling method is neutral across layers. Mean pooling over variable-length windows is a lossy, depth-dependent aggregation: early HuBERT layers produce frame embeddings that vary within a word (phoneme- and speaker-level), so the average is a poor word proxy and alignment is low; later layers produce more uniform frame embeddings within a word, so averaging is more faithful and alignment rises. The curve in Fig. 2 therefore may trace the pooling operator's interaction with layer depth, not an internal conceptual shift. The 'less capacity' claim is also confounded because BERT's per-token embeddings are not aggregated the same way, so the comparison is not apples-to-apples. The citation [10] does not support mean pooling; that work argues for learned pooling. To support the main claim, the authors must show the layer-wise trend is invariant to the aggregation operator.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper investigates how speech, text, and multimodal foundation models form latent concepts. Using Latent Concept Analysis, the authors cluster contextualized word representations (for speech, obtained by mean-pooling frame embeddings within forced-aligned word boundaries) and align the clusters with human-defined taxonomies such as POS, chunking, semantic annotations, suffixation, and sentiment polarity. They report that text models encode linguistic taxonomies from early layers while speech models gradually shift from acoustic to linguistic representations, that speech models allocate less capacity to linguistic and semantic taxonomies, and that fine-tuned speech models underperform in negative sentiment classification compared to text models. The paper releases code and a TTS-generated audio version of SST-2.","tokens_in":8125,"tokens_out":5666,"duration_ms":57104,"significance":"If the findings are robust, the paper provides a valuable layer-wise comparison of conceptual structure across speech and text foundation models, an underexplored area. Strengths include the public release of code and an audio SST-2 collection, use of an established unsupervised concept discovery framework, and concrete qualitative examples. However, the central methodological choice of mean-pooling frames and the absence of direct acoustic taxonomies currently leave the main claims vulnerable to alternative explanations; the manuscript would benefit from additional controls and statistical grounding.","major_comments":[{"comment":"The word-level speech embeddings Ψw are derived by averaging frame embeddings within MFA word boundaries, and this aggregation is used in all subsequent clustering and alignment. The central layer-wise trend in Figure 2—low linguistic alignment in early HuBERT layers with a gradual rise—is interpreted as evidence that speech models transition from acoustic to linguistic representations. However, mean pooling over variable-length windows is a depth-dependent, lossy operation: early-layer frame embeddings are phonetically and speaker-varied within a word, making the average a poor word proxy, while later-layer frame embeddings are more uniform, making the average more faithful. The observed curve may therefore trace the pooling operator's interaction with layer depth rather than an internal conceptual shift. The citation [10] argues for learned pooling and does not support mean pooling. To support the central claim, the authors should show that the layer-wise trend is invariant to the aggregation operator (e.g., first/last frame, attention pooling, or learned pooling) or analyze models that produce word-level representations directly.","section":"2.1 Speech Input"},{"comment":"The claim that speech models 'gradually transition from acoustic to linguistic representations' requires direct evidence of acoustic encoding in early layers and a decline of acoustic alignment in later layers. The paper only measures alignment with linguistic taxonomies (POS, chunking, semantics, suffixation). Low alignment with linguistic taxonomies in early layers is compatible with several alternatives—including the pooling artifact described above, lack of word-level structure, or simply the absence of those particular categories—and does not by itself demonstrate that the representations are acoustic. The authors should include acoustic or phonetic taxonomies (e.g., phoneme classes, speaker or prosodic properties) to directly test the transition.","section":"4.1 Comparing Modalities"},{"comment":"The conclusion that speech models 'allocate less capacity to linguistic and semantic taxonomies' is not supported by the alignment metric used. The metric measures the fraction of discovered clusters that match human-defined categories; it does not measure representational capacity. Differences across HuBERT, BERT, Seamless M4T, and SpeechT5 are confounded by architecture, depth, pretraining objective, and input tokenization, so the capacity interpretation is speculative. A more direct capacity measure (e.g., probing accuracy with controlled probe size, or information-theoretic estimates) or at least a matched comparison of models with similar size and objective would be needed.","section":"4.1 Comparing Modalities"},{"comment":"The coverage term κθ(Cl) is defined with the same condition as the alignment term αθ(Ce): both require |Ce∩Cl|/|Ce| ≥ θ. For a coverage term, one would expect the condition to involve the fraction of the linguistic concept Cl that is covered by an encoded concept Ce, i.e., |Ce∩Cl|/|Cl|. As written, the two terms double-count the same condition and the metric does not measure taxonomy coverage as claimed. This error affects the reported alignment percentages and should be corrected, and the experiments re-run, before the quantitative claims can be accepted.","section":"2.2 Concept Alignment"},{"comment":"The sentiment analysis experiment reports accuracy values (87.48% versus 93.21% for negative; 93.31% versus 94.98% for positive) but does not provide details of the fine-tuning protocol (data split, optimizer, number of runs, seeds) and reports no error bars or significance tests. The claim that speech models struggle to capture negative sentiment rests on a single model (HuBERT) and a single TTS-generated audio corpus. Without additional models, natural speech data, and statistical validation, this conclusion is not robustly supported.","section":"4.2 Task-specific Concepts"}],"minor_comments":[{"comment":"The 'Index Terms' line in the header appears to be a formatting artifact and should be removed.","section":"1 Introduction"},{"comment":"Figure 2 has no legend identifying the different taxonomy lines; a legend or a clear caption description is needed to interpret the plot.","section":"Figure 2"},{"comment":"The text refers to 'base and fine-tuned models' but does not specify which base model variants are used in the main analysis; please clarify.","section":"3 Experimental Setup"},{"comment":"Reference [14] for Seamless M4T lacks complete author and venue information; please provide the full citation.","section":"7 References"},{"comment":"There is a typo: 'paralingusitic' should be 'paralinguistic'.","section":"4.1 Comparing Modalities"},{"comment":"The choices K=600 and θ=0.9 are inherited from [11]; a brief sensitivity analysis or a justification for these values on the current datasets would strengthen the results.","section":"3 Experimental Setup"}],"recommendation":"major_revision","confidential_remarks":"The paper appears to be a conference-style submission (with index terms and a short format) that has been submitted to a journal; the authors should adapt the presentation accordingly. The main technical concern is the mean-pooling confound, which is central to the paper's headline claim. If the authors can demonstrate robustness to alternative pooling methods and add acoustic taxonomies, the paper would be a solid contribution. The coverage-term error in Section 2.2 should be fixed and the experiments re-run. I recommend major revision rather than rejection because the research question is timely and the released artifacts (code, SST2-audio) are useful to the community."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe headline: the paper's central claim about speech models gradually transitioning from acoustic to linguistic representations may be an artifact of mean-pooling frame embeddings. The authors derive word-level speech representations by averaging all frames within forced-aligned word boundaries. That is a lossy, depth-dependent operation. Early HuBERT layers have frame embeddings that vary within a word, so the average is a poor word proxy and alignment with word-level taxonomies is low; later layers produce more uniform frames, so the average is more faithful and alignment rises. The layer-wise curve in Figure 2 may trace the pooling operator's interaction with layer depth, not a conceptual shift inside the model. The stress-test note is correct on this. And citation [10] supports learned pooling, not simple averaging.\n\nWhat the paper does well: it extends Latent Concept Analysis to speech and multimodal models, covering HuBERT, BERT, Seamless M4T, and SpeechT5 across POS, chunking, semantics, and sentiment. Releasing the SST2-audio dataset and code is a concrete contribution. The sentiment asymmetry (speech models weaker on negative sentiment) is interesting, though preliminary.\n\nThe soft spots are substantial. There are no significance tests or multiple runs. Models differ in architecture and depth, so the capacity comparison is confounded. SST2-audio comes from a single TTS system; the negative-sentiment asymmetry could be a prosody artifact of XTTSv2. All fixable, but they matter.\n\nThe central argument does not hold up as stated. Without showing the layer-wise trend is invariant to the aggregation operator, the speech-to-linguistic transition and the capacity gap remain unsupported. That said, the paper is serious, not sloppy. The question is interesting and the resources are reusable. I would send it to peer review with a major-revision recommendation, asking for a learned-pooling control, error bars, and ideally real speech data. A reader working on speech interpretability would get value from the framing and the dataset even now.\n\nBring it to reading group; the pooling issue is a good teaching example of preprocessing changing the conclusion.","headline":"Mean-pooling of speech frames may be manufacturing the paper's central speech-to-linguistic transition; the resources and framing are useful, but the core claim needs re-validation with a different pooling method.","tokens_in":8665,"tokens_out":4161,"would_cite":false,"duration_ms":45029,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Text models form linguistic concepts early; speech models build them late and with less capacity, and they encode negative sentiment more weakly.","keywords":["concept formation","latent concept analysis","speech foundation models","text foundation models","multimodal learning","linguistic taxonomies","sentiment analysis","interpretability"],"falsifier":"Re-run the full concept-discovery and alignment pipeline on the same speech data, but replace frame-averaged word embeddings with word embeddings produced by a learned pooling model or with hand-corrected word boundaries on a sample; if the layer-wise alignment curves for the speech model no longer show the late acoustic-to-linguistic transition, or if the negative-sentiment gap narrows, the central claims rest on the pooling choice rather than on the modality itself.","tokens_in":7751,"feed_emoji":"🔊","tokens_out":7309,"duration_ms":70168,"temperature":0.7,"pith_summary":"This paper tries to establish that concept formation in foundation models is modality-dependent: text models develop linguistic abstractions early and deeply, while speech models spend their early layers on acoustics and only later align with word-level linguistic taxonomies, always devoting less capacity to them. It matters because it offers a mechanistic explanation for a practical gap — speech models underperform text models on language understanding and on negative sentiment — and ties that gap to architecture and training objectives rather than to the input signal alone. The authors reach this conclusion by clustering layer-wise representations into interpretable concepts and measuring how well those concepts match human-defined linguistic categories in both unimodal and multimodal models. A direct consequence of their claim is that better speech reasoning may require not just more data but changes in how speech models are built and trained.","feed_headline":"Speech models lag text models on linguistic concepts","feed_subtitle":"Layer-by-layer clustering shows text encodes grammar early; speech builds it late and misses negative sentiment.","key_machinery":"The central mechanism is Latent Concept Analysis, an unsupervised pipeline that extracts contextualized embeddings from every transformer layer, clusters them with K-means into roughly 600 concepts, and scores each cluster against human-defined taxonomies using an alignment-and-coverage metric at a high match threshold ($\\theta=0.9$). For speech input, frame-level embeddings are averaged within forced-aligned word boundaries to produce word-level acoustic word embeddings, which lets the same clustering and taxonomy alignment run on words in both modalities. The layer-wise alignment curves are the load-bearing evidence: their shape is what supports the acoustic-to-linguistic transition in speech models, the early-and-deep linguistic encoding in text models, and the distinctive plateau in shared-encoder multimodal models.","core_discovery":"The paper's central claim is that text models such as BERT directly encode linguistic taxonomies from the earliest layers, whereas speech models such as HuBERT transition gradually from acoustic to linguistic representations, with linguistic structure emerging in middle layers and peaking in upper layers. Multimodal models do not simply inherit the text-like pattern: SpeechT5's shared encoder, optimized for cross-modal consistency, shows weaker explicit linguistic taxonomy alignment in deeper layers than either BERT or the more loosely coupled Seamless model. The paper also reports a task-specific asymmetry: after fine-tuning for sentiment, both BERT and HuBERT develop polarity concepts in their final layers, but the speech model captures positive sentiment far better than negative, consistent with its lower accuracy on negative examples (87.48% vs 93.21% for BERT, while positive accuracy is comparable at 93.31% vs 94.98%). These findings collectively support the claim that speech models reserve substantial representational capacity for phonetic, prosodic, and speaker-specific features, leaving less room for the explicit linguistic and semantic taxonomies that text models internalize.","pith_inferences":["An implication the paper leaves open is that the negative-sentiment gap in speech models may be partly inflated by the synthetic TTS audio used in the experiment, because synthetic prosody can flatten the acoustic cues that carry negative polarity; testing on natural spontaneous speech would separate alignment artifacts from intrinsic modality differences.","A testable extension is to replace uniform frame averaging with learned or attention-based pooling when building acoustic word embeddings; if the taxonomy-alignment curves shift, the pooling choice is part of the explanation rather than just the modality itself.","The paper's capacity explanation predicts that scaling up a speech model or giving it an explicit text-alignment bottleneck should increase linguistic taxonomy alignment; this can be checked by comparing base and large variants of the same speech model or by ablating SpeechT5's cross-modal objective."],"forward_implications":["Speech foundation models will need additional capacity or pretraining objectives aimed at linguistic structure if they are to match text models on tasks that require syntax and semantics.","The late emergence of linguistic taxonomies in speech models implies that layer choice matters for probing: early layers carry acoustic content, while middle-to-upper layers carry the most linguistic signal.","Multimodal training that forces speech and text into one shared encoder can reduce the depth of explicit linguistic taxonomies relative to text-only or loosely coupled models, revealing a cost to cross-modal alignment.","Fine-tuned speech sentiment models are asymmetric: positive polarity is encoded robustly while negative polarity is under-represented, so speech sentiment systems should be evaluated on negative-heavy test sets separately.","Falling taxonomy alignment in the uppermost layers is interpreted not as lost structure but as a shift toward task-specific and holistic representations, which argues for evaluating final layers on task-level concepts rather than only linguistic taxonomies."],"supporting_citations":[{"why":"Supplies the Latent Concept Analysis method used to discover concepts from contextual embeddings.","marker":"[8]"},{"why":"Defines acoustic word embeddings by averaging frames within word boundaries, the exact pooling used to turn speech frames into words.","marker":"[10]"},{"why":"Provides the alignment-and-coverage metric that maps discovered clusters to linguistic taxonomies and task concepts.","marker":"[11]"},{"why":"Supplies HuBERT, the speech model whose layer-wise concept extraction grounds the acoustic-to-linguistic transition claim.","marker":"[12]"},{"why":"Supplies BERT, the text model whose early and deep taxonomy alignment is the comparison baseline.","marker":"[13]"},{"why":"Supplies Seamless M4T, the multimodal model with separate speech and text encoders used in the comparison.","marker":"[14]"},{"why":"Supplies SpeechT5, the shared-encoder multimodal model whose distinct alignment pattern motivates the cross-modal capacity claim.","marker":"[15]"},{"why":"Supplies the SST-2 sentiment dataset from which the audio version is generated and which defines the positive and negative polarity concepts.","marker":"[19]"},{"why":"Supplies LibriSpeech, the read-speech corpus used for speech concept extraction.","marker":"[20]"}],"fun_headline_variants":["Speech AI learns language late, misses negativity","Text models beat speech on grammar, sentiment","Speech models lag on language, text excels early","Why speech AI can't hear negative sentiment","Spoken language models: weak on negative concepts"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole comparison depends on the assumption that the forced-alignment tool places word boundaries accurately enough that averaging the audio frames within each boundary yields a faithful word embedding; if boundaries are off, the speech clusters and every modality comparison built on them are corrupted.","fun_headline_variants_meta":{"raw":{"variants":["Speech AI learns language late, misses negativity","Text models beat speech on grammar, sentiment","Speech models lag on language, text excels early","Why speech AI can't hear negative sentiment","Spoken language models: weak on negative concepts"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000145,"raw_usage":{"total_tokens":1148,"prompt_tokens":885,"completion_tokens":263,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":501,"completion_tokens_details":{"reasoning_tokens":195}},"tokens_in":501,"tokens_out":263,"duration_ms":3205,"temperature":1.0,"reasoning_tokens":195,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T11:50:06.190895+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the full concept-discovery and alignment pipeline on the same speech data, but replace frame-averaged word embeddings with word embeddings produced by a learned pooling model or with hand-corrected word boundaries on a sample; if the layer-wise alignment curves for the speech model no longer show the late acoustic-to-linguistic transition, or if the negative-sentiment gap narrows, the central claims rest on the pooling choice rather than on the modality itself.","supporting_citations":[{"cited_title":"Chatgpt makes medicine easy to swallow: An exploratory case study on simplified radiology reports,","cited_arxiv_id":null,"evidence_quote":"Supplies HuBERT, the speech model whose layer-wise concept extraction grounds the acoustic-to-linguistic transition claim."},{"cited_title":"Where do you know what you know? the representation of semantic knowledge in the human brain,","cited_arxiv_id":null,"evidence_quote":"Supplies BERT, the text model whose early and deep taxonomy alignment is the comparison baseline."},{"cited_title":"Discovering latent concepts learned in BERT,","cited_arxiv_id":null,"evidence_quote":"Supplies Seamless M4T, the multimodal model with separate speech and text encoders used in the comparison."},{"cited_title":"Asking without telling: Exploring latent ontologies in contextual representations,","cited_arxiv_id":null,"evidence_quote":"Supplies SpeechT5, the shared-encoder multimodal model whose distinct alignment pattern motivates the cross-modal capacity claim."},{"cited_title":"BERT: Pre- training of deep bidirectional transformers for language under- standing,","cited_arxiv_id":null,"evidence_quote":"Supplies the SST-2 sentiment dataset from which the audio version is generated and which defines the positive and negative polarity concepts."},{"cited_title":"Seamlessm4t: Massively multilingual & multimodal machine translation,","cited_arxiv_id":null,"evidence_quote":"Supplies LibriSpeech, the read-speech corpus used for speech concept extraction."}],"review_version":1}