Pragmatic Transcript Normalization

• Pragmatic Transcript Normalization is an approach that refines written transcripts by selectively recasting or suppressing disfluencies to preserve the intended communicative intent.
• Empirical studies reveal that normalized transcripts closely match audio perceptions of certainty, reducing misinterpretations caused by hesitancies like 'um' and 'er'.
• The method employs a deterministic pipeline integrating probabilistic token tagging and rule-based recasting, enhancing transcript accuracy for applications in ASR, TTS, and linguistic research.

Pragmatic Transcript Normalization (PTN) is an approach to constructing written records of spoken language that preserves the intended communicative function by recasting or suppressing elements—such as hesitancies and disfluencies—that otherwise trigger altered or misleading inferences in readers. PTN is motivated by empirical findings that specific speech phenomena (e.g., filled pauses like “um,” “er”) encode distinct pragmatic signals when uttered in speech compared to their raw textual transcription. PTN systematically distinguishes between “pause-for-thought” and “expression-of-uncertainty,” striving to neutralize misleading collateral signals while faithfully representing propositional content. This yields transcripts that more accurately reflect a speaker’s stance, authority, and certainty, supporting both linguistic research and downstream applications in ASR/TTS pipelines, human-in-the-loop communication, and social sciences (Collins et al., 2016, Tyagi et al., 2021, Sproat et al., 2016).

1. Functional Distinctions and Pragmatic ^^^^1^^^^oding in Transcripts

PTN is grounded in the distinction between primary messages, which encode propositional content, and collateral messages, which are metacommunicative signals incidentally transmitted via pragmatic tokens. Hesitancies such as “um,” “uh,” “er,” and “uhm” operate in speech as planning signals (“please wait while I think”) and rarely induce perceptions of substantive doubt among listeners. In written transcripts, however, the same tokens are interpreted as markers of uncertainty or lack of authority, causing readers to infer that the speaker is unconfident or uninformed.

This context dependence exemplifies the general phenomenon that the same lexical material can carry systematically different meanings across media—a distinction formalized in ^^^^1^^^^lark & Fox Tree’s model of primary vs. collateral message (Collins et al., 2016). PTN’s objective is to preserve the primary message of the utterance and to selectively recast, suppress, or tag collateral signals (e.g., filled pauses, discourse markers), specifically those likely to mislead text readers.

2. Experimental Evidence and Quantitative Metrics

Empirical validation of PTN principles is demonstrated by controlled studies employing perception experiments. Using interview extracts laden with hesitancies and constructing four experimental conditions—audio and transcript versions for two extracts—researchers quantified reader/listener perceptions of certainty using a 1–9 Likert scale (Collins et al., 2016). Lay participants rated audio (A1 = 8.71, A2 = 7.86) as far more certain than corresponding transcripts (T1 = 3.93, T2 = 3.36), yielding a substantial mean difference Δ ≈ –4.65, with clean transcripts (hesitancies removed) eliminating this gap (audio ≈ text ≈ 6.75). Expert raters showed a reduced effect, but the misalignment in lay perception was robust and uniform (order effects ≤ 0.5). This result underlines the necessity of selective normalization.

Metrics derived from these experiments include:

• Mean difference in perceived uncertainty: $D = \mu_{\text{transcript}} - \mu_{\text{audio}}$
• Token-based probability of uncertainty:

$$P(\text{uncertainty} \mid \text{token}) = \frac{\text{count}(\text{speaker judged “uncertain”} \land \text{token occurred})}{\text{count}(\text{token occurred})}$$

These token-level probabilities inform normalization decisions by quantifying the risk of a given element being interpreted as indicative of uncertainty when transcribed, thus motivating algorithmic intervention.

3. Algorithmic Framework for Pragmatic Transcript Normalization

PTN employs a deterministic multi-step pipeline for normalization (Collins et al., 2016):

Input: Raw transcript $T_{\text{raw}}$ with tokens including filled pauses, disfluencies

Algorithm:

1. Tag and count tokens: For each token $t$ in $T_{\text{raw}}$, increment count, and consult a precomputed $P(\text{uncertainty} \mid t)$ lexicon.
2. ^^^^1^^^^lassification: With threshold $\theta_{\text{uncertainty}}$ (e.g., $\theta$ = 0.5), label $t$ as “uncertainty marker” if $P(\cdot) \geq \theta$ or as “planning marker” otherwise.
3. Normalization rules: For “planning markers,” replace with standardized pause symbols (e.g., “[pause]”, “[pause=0.5 s]”); for “uncertainty markers,” retain as “(uncertain),” “[?],” or inline tags. Adjacent clauses may be bracketed as “(speaker seems tentative: …).”
4. Post-processing: ^^^^1^^^^ollapse consecutive pauses, remove extraneous whitespace, and ensure editorial consistency.

Pseudo-code:

for token in T_raw.tokens: if token in filled_pause_list: p = P_uncertainty[token] if p >= theta: replace token with "<uncertain>" else: replace token with "[pause]" normalize_repeated_pauses(T_raw)

This approach can be generalized via the assignment of per-token probabilities for other pragmatic markers, enabling consistent normalization across diverse pragmatic phenomena.

4. Text Normalization Paradigms and Sequence Tagging Architectures

Text normalization (TN) as implemented in contemporary systems such as Proteno (Tyagi et al., 2021) is formulated as a token-level sequence classification problem. Given tokenized input $x = (x_1, ..., x_n)$, the model predicts normalization class labels $y = (y_1, ..., y_n)$, each corresponding to deterministic or sample-driven verbalization functions. Proteno exploits granular (character class–driven) tokenization, facilitating detailed token-class mapping (e.g., splitting "C3PO" → ["^^^^1^^^^", "3", "PO"], "1/1/2020" → ["1", "/", "1", "/", "2020"]). Mappings are learnt via two mechanisms:

• Hard-coded classes for common semiotic types (self, silence, digit, cardinal, ordinal, etc.)
• Auto-generated classes from training data for rare/irregular mappings (abbreviations, domain-specific forms)

The probabilistic model estimates $P(y \mid x)$; in practice, linear-chain ^^^^1^^^^RFs, Bi-LSTM-^^^^1^^^^RF hybrids, and Transformer taggers are deployed. For each token $x_i$, inference applies a masked softmax so only valid classes (as filtered by Accept($x_i$)) are scored, reducing erroneous outputs. Minimal hand-coded rules cover morphophonological alternations and realignment; the dominant annotation burden is absorbed via a small set of AG classes that are automatically extracted from labeled data.

5. Evaluation Methodology and Success ^^^^1^^^^riteria

Evaluation of PTN hinges on direct perception experiments, leveraging Likert-scale judgments of certainty before and after normalization interventions. The criteria for success are:

• The mean difference in certainty ratings between normalized transcript and audio, $$D_{\text{norm}} = \mu_{T_{\text{norm}}} - \mu_{\text{Audio}}$$, should target $|D_{\text{norm}}| \leq \Delta_{\max}$ (e.g., $\Delta_{\max}=0.5$).
• Paired t-tests ($$t_{\text{norm}} = D_{\text{norm}} / \text{SE}(D_{\text{norm}})$$) confirm non-significance ($p > 0.05$) of residual gaps.
• Secondary measures include distractor ratings (friendliness, authority, etc.) and qualitative feedback, ensuring that normalization does not introduce awkwardness or reduce transcript readability (Collins et al., 2016).

In token-level sequence normalization tasks (Proteno (Tyagi et al., 2021)), word error rate (WER)—the normalized character-level Levenshtein distance between system output and reference—is the principal metric. Reported WERs approach or outperform prior systems despite utilizing significantly reduced annotated data (as low as 0.89% WER on Spanish test data).

6. Hybrid and Data-Driven Architectures for Robust Transcript Normalization

Data-driven RNN approaches, as evaluated in Sproat & Jaitly (Sproat et al., 2016), are capable of achieving overall accuracies >99% in text normalization but remain susceptible to “semantic drift” in high-stakes semiotic domains (e.g., mislabeling measurement units). To address systematic errors that carry substantive consequences, the integration of finite-state transducer (FST) filtering is proposed:

• For each input token, a semiotic-class-specific WFST constraint enforces strict admissibility and overgenerates legal verbalizations, pruned by beam search with cost augmentation.
• RNN+FST hybrids eliminate critical substitution errors (e.g., “two mA” → “two units” barred), with no loss on unconstrained categories.
• In practice, RNN+FST achieves class-wise improvements (MEASURE: 97.2% → 99.3%, MONEY: 97.2% → 100% for test sets), demonstrating the necessity of hand-engineered constraints for robust PTN (Sproat et al., 2016).

Guidelines for real-world deployment include constructing concise class-specific FSTs for high-stakes tokens and using the RNN for context and generalization. Bootstrapping to new domains or languages is achieved by annotating small lexicons and leveraging transfer learning.

7. Generalization: Language ^^^^1^^^^ode Analysis and Extension to Pragmatic Markers

PTN is subsumed under a broader research agenda termed Language ^^^^1^^^^ode Analysis (L^^^^1^^^^A), which seeks to codify how meanings shift between modalities and symbol systems. The threshold-and-rule framework is extensible:

• Intonation markers (e.g., rising terminals) are tagged to preserve stance, with $P(\text{ambivalent} \mid \text{marker})$ guiding neutralization or retention.
• Discourse markers (“you know,” “I mean”) are normalized or tagged based on empirical $P(\text{attitude }|\text{marker})$.
• Non-lexical sounds (laughter, sighs, throat-clears) are annotated as [laughter], [sigh], etc., when they contribute affect or stance information.

Building a probabilistic lexicon of pragmatic tokens $T = \{t_1, t_2, \ldots\}$ with associated normalization logic enables systematic transcript transformation across media—speech, Morse, tweets—ensuring that collateral and primary messages remain aligned with communicative intent (Collins et al., 2016).

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References:

• “Um, er: How meaning varies between speech and its typed transcript” (Collins et al., 2016)
• “Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems” (Tyagi et al., 2021)
• “RNN Approaches to Text Normalization: A ^^^^1^^^^hallenge” (Sproat et al., 2016)