.png){kind=logo}
"Could you please convert this English podcast into Japanese?"
It seemed like a straightforward request from one of our clients. The goal was simple: create Japanese versions of English interview podcasts featuring prominent financial leaders to promote their upcoming international symposium in Tokyo. Yet this apparently simple request opened up a fascinating journey through natural language processing’s Uncanny Valley.
Most of us have encountered the Uncanny Valley through computer-generated images in movies — those almost-but-not-quite-human characters that make us slightly uncomfortable. The same phenomenon exists in AI-generated speech, particularly when crossing language barriers.
Our initial attempt at direct English-to-Japanese AI dubbing of an interview with Elizabeth Fernando, Chief Investment Officer at NEST (National Employment Savings Trust, UK's largest pension scheme), illustrated this perfectly.
While the AI could handle the complex financial vocabulary, the Japanese pronunciations sounded like someone who had studied Japanese for a semester attempting to discuss sophisticated pension reform policies—technically correct but fundamentally unnatural.
The root of this problem lies in the mathematical structures underlying language. English and Japanese are fundamentally different in their grammatical architecture. Where English follows a subject-verb-object pattern (SVO), Japanese typically arranges sentences as subject-object-verb (SOV). This isn't merely an academic distinction—it creates real challenges for AI translation systems. In our initial tests, we observed the AI attempting to simultaneously maintain the original English word order while trying to conform to Japanese grammar rules, resulting in what I can only describe as computational linguistic dissonance.
Consider the term "NEST" from Fernando's interview. A direct AI translation attempt resulted in the system struggling to preserve the English pronunciation while converting it to a more natural Japanese phonetic pattern, "Nesto." This single example encapsulates the broader challenge: how do we maintain technical accuracy and institutional recognition while achieving natural-sounding speech?
In fact, this challenge is symptomatic of a larger issue when it comes to handling language that mixes English terms within Japanese speech.
Modern Japanese has incorporated many English words, but these words are pronounced following specific Japanese phonetic rules (katakana). AI systems can often fail to recognize this distinction when translating from written text to voice, leading to unnatural or awkward pronunciation.
Consider another example: "computer." In Japanese, this becomes "コンピュータ" (konpyūta), a word pronounced with distinct Japanese phonetics. If AI encounters this in text-to-voice translation, it might try to use the native English pronunciation, "computer," which would feel off in an otherwise natural Japanese conversation. The AI could equally attempt a too-literal translation that sounds unnatural in both languages.
When translating, the AI might default to a pure English pronunciation, which sounds foreign to Japanese speakers, or attempt a direct translation where an English word is treated like a typical Japanese word, failing to account for the nuances of how borrowed terms are spoken. This is especially tricky when crossing from text to voice or when the transcription is not entirely clean.
The technical solution we developed combines several generative AI components and human inputs to produce natural-sounding translations while maintaining technical accuracy.
The core architecture addresses three primary technical requirements:
Language Structure Translation English and Japanese follow different grammatical patterns (SVO vs. SOV). Rather than attempting direct audio translation, we implemented a two-stage process:
GPT-based text translation with optimized prompting for financial terminology
Human QA layer for technical term verification. This approach handles the structural differences while preserving technical accuracy.
Voice Synthesis Implementation. Using the Murf.ai API, we developed a synthesis pipeline that includes:
Speaker-specific voice profile mapping
Rolling 3-paragraph context window for natural flow
Automated pause pattern insertion based on linguistic context
Technical Term Management. Financial terminology requires consistent handling. We implemented rule-based processing for different categories:
Institution names (e.g., "NEST"): Preserved in English with Japanese context
Financial concepts: Translated using industry-standard terminology
Hybrid terms: Context-dependent handling based on usage patterns
The implementation architecture:
Copy Input Processing:
Transcript segmentation
Speaker identification
Technical term tagging
Translation Pipeline:
GPT translation with context preservation
Human QA for technical accuracy
Voice synthesis with speaker profiles
Output Integration:
Sequential audio assembly
Cross-fade implementation
Quality verification
The key insight was understanding that we didn't need to solve for 100% perfection. By identifying the critical 5% of content that required human expertise (like handling terms such as "NEST") and automating the remaining 95%, we created a scalable solution that bridges the Uncanny Valley while maintaining efficiency—and it’s definitely more cost-efficient than hiring natural Japanese speaking voice actors to re-record the podcasts.
This methodology has implications far beyond financial services. The same mathematical principles can be applied to any domain requiring sophisticated cross-language communication. By understanding where human expertise is truly necessary and where AI can effectively handle the heavy lifting, we can create scalable solutions that feel natural rather than uncanny.
The mathematics of natural speech today isn't about achieving perfect translation—it's about optimizing the balance between automation and human oversight.
Contact Us to learn more.
Pure Math Editorial is an all-purpose virtual writer we created to document and showcase the various ways we are leveraging generative AI within our organization and with our clients. Designed specifically for case studies, thought leadership articles, white papers, blog content, industry reports, and investor communications, it is prompted to ensure clear, compelling, and structured writing that highlights the impact of AI across different projects and industries.