Voice AI & Speech ResearchSeptember 16, 20268 min read

Sub-300ms Conversational Voice Turn-Taking and Grapheme-to-Phoneme (G2P) Conjunct Disambiguation in Bangladeshi Bengali

Engineering studio-quality neural speech synthesis for South Asian languages: overcoming conjunct phonetic ambiguities, Banglish code-switching, and full-duplex telephony latency.

Dr. Nazmul Huda

Head of Acoustic ML Research

1. The South Asian Acoustic Barrier

Bengali is spoken by over 250 million people, yet most global commercial speech engines treat it with robotic, flat prosody and severe phonetic mispronunciations.

Western acoustic models trained on English phoneme inventories stumble when pronouncing complex Bengali consonant conjuncts (যুক্তবর্ণ), nasal vowels, and loanwords, creating an unnatural customer experience in voice automated systems.

Zunn Sono was engineered from scratch with a custom Bengali acoustic foundation model and grapheme-to-phoneme (G2P) engine tuned for authentic Dhaka colloquial cadence.

2. Bengali G2P & Conjuncts

In Bengali orthography, letters combine into ligatures where the phonetic realization differs radically from the constituent letters (e.g., ক্ষ sounding as 'kkh', জ্ঞ sounding as 'ggo').

Our proprietary G2P engine maps written text into International Phonetic Alphabet (IPA) representations with context-aware disambiguation rules, ensuring that conjuncts, schwa deletion, and nasalization are rendered accurately.

Bengali G2P Phoneme Normalization Pipeline

interface G2PToken {
  grapheme: string;
  ipaPhonemes: string[];
  stress: "primary" | "secondary" | "neutral";
  nasalized: boolean;
}

function normalizeBengaliG2P(text: string): G2PToken[] {
  // 1. Expand standard contractions & abbreviations
  const expanded = expandAbbreviations(text);
  // 2. Disambiguate conjuncts (e.g. ক্ষ, জ্ঞ, হ্ম)
  const disambiguated = resolveConjunctLigatures(expanded);
  // 3. Apply schwa-deletion context rules
  return applyDhakaProsodyRules(disambiguated);
}

3. Handling Banglish Code-Switching

In modern South Asian commerce, callers constantly switch between Bengali and English within the same sentence ('Amar order er delivery status ta kindly check korun').

Sono features unified bilingual acoustic modeling that transitions between Bengali and English phoneme spaces without awkward pitch shifts, robotic gaps, or mispronounced English technical terms.

4. Sub-300ms Conversational Latency

Natural conversation breaks down when latency exceeds 500ms. In telephony networks, a caller will talk over the AI agent if latency is high.

Sono streams audio over bidirectional WebSockets and SIP trunks, pairing streaming voice activity detection (VAD) with low-latency neural acoustic decoding to achieve sub-300ms voice-to-voice turn-taking.

5. Voice Captcha & Watermarking

To prevent unauthorized voice cloning and impersonation fraud, Sono introduces Voice Captcha—requiring dynamic live speaker verification challenges—and embeds imperceptible cryptographic neural watermarks into every synthesized audio stream.