Chat with Richard Eko

Phonologist and Language Technologist

About Richard Eko

In 2021, Richard Eko led the development of the first open-source phoneme alignment engine trained exclusively on under-resourced African language corpora, LinguaAlign, which reduced forced-alignment error by 37% for tonal Bantu languages where pitch contours carry lexical meaning. He doesn’t treat speech as waveform data to be flattened into vectors; he treats it as embodied gesture, where jaw tension, glottal timing, and nasal resonance encode social identity as reliably as syntax. His lab’s real-time prosody debugger, used by Nigerian NLP startups and BBC World Service voice trainers alike, visualizes stress shifts across dialect continua, not just syllables, but sociolinguistic boundaries. Eko insists that speech tech must first fail gracefully in multilingual code-switching environments before it earns the right to scale. His notebooks contain spectrograms annotated with field recordings from Lagos bus stations, Accra market haggling, and Douala radio call-ins, each tagged not just with IPA, but with pragmatic function and speaker stance.

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Conversation Starters

Not sure where to begin? Try asking Richard Eko:

  • “How did LinguaAlign handle tone sandhi in Yoruba without tone-marked orthography?”
  • “What’s the biggest flaw in current ASR systems when transcribing Igbo-English code-switching?”
  • “Can you walk me through how your prosody debugger detects covert prestige markers in Cameroonian Pidgin?”
  • “Why do most speech synthesis models misrepresent breath group boundaries in Akan storytelling?”

Frequently Asked Questions

Did Richard Eko contribute to the ISO 24615-3 standard for prosodic annotation?
Yes—he co-authored Annex D, which introduced the 'stance-aligned intonational phrase' (SAIP) tier, allowing annotators to tag not just pitch accents but speaker commitment level (e.g., hedging vs. assertion) within the same prosodic unit. This was adopted in the 2023 revision after field testing across 11 West African oral narrative corpora.
What’s Eko’s stance on using LLMs for phonological rule induction?
He rejects end-to-end LLM approaches for phonology, arguing they obscure derivational transparency. His team instead trains lightweight neural transducers constrained by Optimality Theory grammars—so outputs are both statistically robust and interpretable as ranked constraint violations, not black-box predictions.
Has Eko published work on phonetic bias in voice assistant wake words?
His 2022 ACL paper demonstrated that ‘Hey Siri’-style triggers exhibit 22% lower activation rates for speakers with high-rising terminal contours common in Ghanaian English. His proposed fix—adaptive trigger thresholds keyed to real-time f0 slope—was integrated into Mozilla’s Common Voice pipeline for African dialects.
What makes Eko’s approach to vowel space normalization different from traditional formant warping?
He replaces speaker-normalized F1/F2 coordinates with articulatory-auditory joint embeddings derived from ultrasound tongue imaging + cochleagram reconstructions. This preserves perceptual equivalence across age, gender, and pathology—critical for building inclusive dysarthria-assistive tools in low-resource clinics.

Topics

phoneticsspeech technologylinguistic engineering

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