Spanglish, Freudian Slips, Levenshtein, and Safe Signal Drops

How Levenshtein distance, phonetic bias rules, and a Freudian valence layer can generate Spanglish-style respellings — and how to responsibly drop accidental mental-health signals to minimize false positives.

Author: Juan Rodriguez — Draft

Introduction

Spanglish—where English and Spanish phonology, vocabulary, and orthography meet—produces characteristic pronunciations and respellings: feisbuk, eschool, filin. Layered on top of this sociophonetic play is another phenomenon: the Freudian slip. Some near-miss substitutions are phonetically plausible and simultaneously emotionally charged (e.g., beach → bitch), creating a rich creative space.

This post describes a compact computational model: Levenshtein distance provides an edit-cost backbone, a set of Spanglish phonetic bias rules proposes orthographic candidates, and a small Freudian lexicon boosts emotionally salient near-misses. Critically, we add a conservative, auditable “false-positive drop” (FP drop) layer: when candidate outputs or inputs resemble mental-health signals, the system reduces automatic signal strength to avoid mislabeling, routes high-risk cases to human review, and logs decisions for auditing.

Why combine these pieces?

Levenshtein distance gives a useful, language-agnostic numeric metric for surface similarity (small edits imply plausibility). But orthography alone is blind to phonology and sociolinguistic tendencies. By seeding the candidate space with phonetic bias rules tailored for Spanish-English interference and adding a Freudian bias layer, we generate outputs that feel both phonetically plausible and psychologically resonant.

Practical motivation: this system can help creative writers craft bilingual dialogue, help researchers simulate noisy spellings for robust NLP training, and produce playful interactive art. At the same time, it must avoid generating or amplifying false clinical inferences—hence the FP-drop layer.

Model components — overview

Phonetic-bias rule set — string-based substitutions and repairs that approximate Spanish-influenced pronunciations.
Candidate generation — rule-applied variants + small orthographic perturbations (insertions, deletions, substitutions).
Levenshtein filter & ranking — discard distant candidates, prefer smaller edit distances.
Freudian-valence lexicon — an optional bias map that gives emotionally-charged near-homophones a score boost.
FP-drop safety layer — detects possible mental-health triggers and applies a conservative, tunable penalty to reduce false positives and escalate where needed.

Phonetic bias rules (compact example)

These rules are intentionally small and easy to experiment with; expand with actual phoneme-aware tools later.

Consonant swaps: v ↔ b, z ↔ s, th → t
Epenthesis: insert e before s-clusters (school → eschool)
Vowel simplification: ee/ea → i, ou → u
Overphonetic spellings: feeling → filin, because → bekos

Note: these are heuristic, not prescriptive. Spanglish varies widely across speakers and regions.

Freudian valence lexicon (example)

A tiny lexicon that lists neutral targets and preferred taboo-ish near-miss candidates. This is used only to bias ranking, not to force substitutions.

Neutral target	Biased candidate	Reason
beach	bitch	phonetic proximity + taboo valence
sheet	shit	minimal pair + expletive
focus	fuckus / fokus	near sound, sexual profanity
success	sucksess	ambition vs. frustration double entendre

False-Positive Drop (FP drop): design goals

When candidate outputs or the input text contain phrases that resemble mental-health expressions (e.g., "I want to die", "I can't cope"), automated systems risk producing harmful false-positive inferences. Our FP-drop component aims to:

Conservatively reduce the system’s automatic confidence for any candidate associated with mental-health triggers.
Never automatically silence extremely high-severity cases; instead escalate to a human-in-the-loop.
Make the penalty auditable and tunable, and record context so reviewers understand why a candidate was suppressed.

FP-drop penalty — formula and rationale

The FP-drop is a value between 0 and 0.95 (0 = no drop; 0.95 ≈ near-complete suppression). It is computed from:

base_rate_estimate — historic false-positive rate for the trigger phrase (higher → larger drop)
confidence — confidence of the mental-health detection model (higher confidence reduces drop, conservatively capped)
severity_weight — clinician-labeled severity (0 low – 1 high)
context_factor — presence of first-person or immediacy markers that could reduce the drop

Suggested conservative formula:

FP_drop = clamp( w1 * base_rate + w2 * (1 - confidence) + w3 * severity - w4 * context_factor, 0, 0.95 )

Recommended starting weights: w1=0.45, w2=0.25, w3=0.25, w4=0.15. Tune with clinician-reviewed labeled data.

Pipeline — where FP-drop sits

Input word(s) → generate candidate variants via phonetic rules + orthographic edits.
Compute Levenshtein distance to filter plausible candidates (edit distance ≤ T).
Score candidates: base scoring can be a weighted function of 1/distance, phonetic similarity, freudian boost.
Apply FP-drop if input or candidate matches mental-health triggers; compute adjusted_score = base_score * (1 - FP_drop).
Make decision: (a) show candidate with adjusted confidence, (b) suppress and flag for human review, or (c) if high-severity and adjusted_score still above threshold, escalate immediately.

Python — integrated prototype

This code extends the earlier prototype by adding FP-drop detection, computation, and escalation stubs. It's intentionally dependency-free for portability.

# spanglish_freudian_safe.py — prototype with FP-drop

from collections import defaultdict
import time

# --- Levenshtein (same DP implementation) ---
def levenshtein(a, b):
    if a == b: return 0
    la, lb = len(a), len(b)
    prev = list(range(lb+1))
    for i, ca in enumerate(a, start=1):
        cur = [i] + [0]*lb
        for j, cb in enumerate(b, start=1):
            ins = prev[j] + 1
            delete = cur[j-1] + 1
            sub = prev[j-1] + (0 if ca == cb else 1)
            cur[j] = min(ins, delete, sub)
        prev = cur
    return prev[lb]

# --- small phonetic rule set ---
PHONETIC_RULES = [('v','b'),('b','v'),('z','s'),('s','z'),('th','t'),('ee','i'),('ou','u')]

def apply_rules(word):
    variants = set([word])
    for a,b in PHONETIC_RULES:
        if a in word:
            variants.add(word.replace(a,b))
    # epenthesis
    if word.startswith('s') and len(word)>1 and word[1] not in 'aeiou':
        variants.add('e' + word)
    return variants

# --- small freudian lexicon ---
FREUDIAN = {'beach': ['bitch'], 'sheet': ['shit'], 'focus': ['fuckus','fokus'], 'success': ['sucksess']}

# --- mental-health triggers (clinician-reviewed ideally) ---
MENTAL_HEALTH_TRIGGERS = {
    'i want to die': 1.0,
    'kill myself': 1.0,
    'cant cope': 0.8,
    'i feel hopeless': 0.7,
    'i am depressed': 0.6,
}

FP_BASE_RATE = defaultdict(lambda: 0.5, {
    'i want to die': 0.15,
    'cant cope': 0.45,
    'i feel hopeless': 0.55,
})

# --- scoring functions ---
def base_score(orig, cand, freud_map, alpha=1.0, beta=1.0, gamma=2.0):
    d = levenshtein(orig, cand)
    if d == 0:
        return 0.0
    phonetic_sim = 1.0 / (1 + d)
    freud_boost = gamma if cand in freud_map.get(orig, []) else 0.0
    return alpha * (1.0 / d) + beta * phonetic_sim + freud_boost

def detect_mh_trigger(text):
    t = text.lower()
    for phrase, sev in MENTAL_HEALTH_TRIGGERS.items():
        if phrase in t:
            return phrase, sev
    return None, 0.0

def compute_fp_drop(trigger_phrase, severity, confidence, w1=0.45, w2=0.25, w3=0.25, w4=0.15):
    base_rate = FP_BASE_RATE.get(trigger_phrase, 0.5)
    context_factor = 0.0  # compute from context heuristics (first-person, immediacy) in production
    raw = w1*base_rate + w2*(1.0 - confidence) + w3*severity - w4*context_factor
    return max(0.0, min(raw, 0.95))

def escalate_to_human(audit):
    # placeholder: write secure audit log & notify reviewers
    # In production, implement encrypted storage + secure queue + rate limits
    print("[ESCALATE] human review required:", audit)

def generate_transforms_safe(word, T=3, top_n=6, mh_confidence=0.6):
    cand_set = set()
    cand_set |= apply_rules(word)
    for f in FREUDIAN.get(word, []):
        cand_set.add(f)
    # simple orthographic edits (naive)
    for i in range(len(word)):
        cand_set.add(word[:i] + word[i+1:])  # deletion
        for c in 'abcdefghijklmnopqrstuvwxyz':
            cand_set.add(word[:i] + c + word[i+1:])  # substitution
    scored = []
    for c in cand_set:
        d = levenshtein(word, c)
        if 0 < d <= T:
            base = base_score(word, c, FREUDIAN)
            # detect trigger in orig or candidate
            trigger, severity = detect_mh_trigger(word)
            if not trigger:
                trigger, severity = detect_mh_trigger(c)
            if trigger:
                fp_drop = compute_fp_drop(trigger, severity, mh_confidence)
                adjusted = base * (1.0 - fp_drop)
                if adjusted >= 2.5 and severity >= 0.8:
                    audit = dict(orig=word, cand=c, trigger=trigger, base=base, adjusted=adjusted, fp_drop=fp_drop, ts=time.time())
                    escalate_to_human(audit)
                scored.append((adjusted, c, d, {'trigger':trigger,'sev':severity,'fp_drop':fp_drop}))
            else:
                scored.append((base, c, d, None))
    scored.sort(reverse=True, key=lambda x: x[0])
    return scored[:top_n]

# Example usage (if run as script):
if __name__ == '__main__':
    for w in ['beach','focus','feeling','i feel hopeless','facebook']:
        print("Input:", w)
        for s, c, d, meta in generate_transforms_safe(w):
            print(f"  {c:14s} d={d} score={s:.3f} meta={meta}")
        print()

This prototype logs when escalation would occur. In production: replace print() with secure audit storage and reviewer notification.

JavaScript — client-side FP-drop UI behavior

For interactive demos you may want a browser-side suppression that reduces visible confidence and optionally shows a neutral "flagged for review" card. Keep sensitive detection server-calibrated; client-side only as UX convenience.

// spanglish_freudian_ui.js — minimal FP-drop UX snippet

function levenshtein(a,b) {
  if (a===b) return 0;
  const la=a.length, lb=b.length;
  let prev = Array.from({length:lb+1}, (_,i)=>i);
  for (let i=1;i<=la;i++){
    let cur=[i];
    for (let j=1;j<=lb;j++){
      const ins = prev[j]+1;
      const del = cur[j-1]+1;
      const sub = prev[j-1] + (a[i-1]===b[j-1]?0:1);
      cur[j] = Math.min(ins,del,sub);
    }
    prev = cur;
  }
  return prev[lb];
}

const TRIGGERS = {'i want to die':1.0,'kill myself':1.0,'cant cope':0.8,'i feel hopeless':0.7,'i am depressed':0.6};

function detectMH(text) {
  const t=text.toLowerCase();
  for (const p in TRIGGERS) if (t.includes(p)) return {phrase:p, severity:TRIGGERS[p]};
  return null;
}

function computeFPDropJS(triggerPhrase, severity, confidence, baseRate=0.5){
  const w1=0.45,w2=0.25,w3=0.25,w4=0.15;
  const contextFactor=0.0;
  let raw = w1*baseRate + w2*(1-confidence) + w3*severity - w4*contextFactor;
  raw = Math.max(0, Math.min(raw, 0.95));
  return raw;
}

function presentCandidate(orig, cand, baseScore, confidence){
  const t = detectMH(orig) || detectMH(cand);
  if (t){
    const drop = computeFPDropJS(t.phrase, t.severity, confidence);
    const adjusted = baseScore * (1 - drop);
    if (drop > 0.6){
      showFlaggedCard(orig, cand, t.phrase);
    } else {
      showCandidateWithLowConfidence(cand, adjusted, t.phrase);
    }
  } else {
    showCandidateNormal(cand, baseScore);
  }
}

// Note: showFlaggedCard / showCandidateWithLowConfidence are app UI functions you implement.

Evaluation and calibration

The FP-drop layer needs calibration using clinician-reviewed labeled examples. Key steps:

Collect a labeled review set where reviewers mark true/false positives for a range of trigger contexts.
Estimate per-trigger false-positive rates (update FP_BASE_RATE dynamically with smooth Bayesian updating).
Tune weights (w1..w4) to achieve acceptable precision while preserving recall for high-severity triggers that need escalation.
Run audits and periodic human review to avoid drift and ensure cultural/contextual sensitivity.

Monitoring metrics: per-trigger precision/recall, aggregate false-discovery rate, number of escalations, reviewer throughput, and user complaints or appeals.

Ethics, policy, and responsible deployment

Two ethical guardrails are non-negotiable:

No automated clinical conclusions: never use this system to make or imply diagnoses. Treat outputs as non-diagnostic signals requiring human confirmation.
User consent & privacy: only run mental-health flagging when users have consented where legally required; minimize retention, encrypt logs, and apply access controls. Follow HIPAA/GDPR as applicable.

Practically: if a candidate is suppressed due to FP-drop, surface a neutral message to the user ("This result was flagged for review") rather than a diagnostic label. Provide opt-in resource links (hotlines) only if the user has consented to receive them.

Applications and limits

Use cases:

Creative writing — generate believable bilingual dialogue with intentional near-miss slips.
Data augmentation — produce noisy spellings to train robust NLP/ASR models for Spanglish speakers.
Interactive art or poetry — create Freudian-phonic pieces that play on taboo adjacency.

Limits:

This prototype is orthography-first. For linguistic precision, convert orthography → phonemes (IPA/ARPAbet) and compute phonetic distance instead of raw string edits.
Freudian interpretation is speculative and culturally bound. Avoid over-interpretation in real-world analysis.
FP-drop reduces harm from automated mis-inference but cannot eliminate the need for expert human judgment.

Next steps (practical roadmap)

Curate a clinician-reviewed trigger lexicon and severity labels.
Implement secure human-review flow with encrypted audit logs and reviewer interfaces.
Swap orthographic heuristics for an O→P (orthography-to-phoneme) step using an established library when moving to production.
Collect labeled review data and iteratively tune FP-drop weights and thresholds.
Provide user controls for consent and clear privacy disclosures.

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