[Design-to-Deployment Roadmap] Build “Real-Time English Calls in Your Own Voice While Speaking Japanese” — Requirements, Architecture, Code Samples, Evaluation, Ops & Ethics

Quick Summary (Inverted Pyramid Style)

• Goal: The speaker talks only in Japanese, and the recipient hears English in the speaker’s own voice tone, enabled via server/smartphone/SIP/WebRTC.
• Minimum Viable Setup (MVP): A WebRTC app (or SIP gateway) + streaming ASR (JA→text) + translation (text→EN) + cross-lingual TTS (EN→voice with speaker embedding). Low latency requires VAD/chunking/streaming synthesis.
• Latency Target: 600–900ms one-way (conversational threshold ≦1.2s). Breakdown: VAD 80ms / ASR 150–250ms / MT 60–120ms / TTS 150–250ms + network/jitter.
• Consent & Safety: Speaker identity must be protected with explicit consent, identity verification, and encrypted speaker embedding. Calls must begin with a translation disclaimer, and recording/transcript policy should be pre-defined.
• Who Benefits: Sales/CS with international clients, travelers/students abroad, multinational families, supportive communication in healthcare/education, and accessibility for the hearing/language-impaired.
• Five Success Factors: ① Streamed segmentation, ② ASR/MT/TTS parallelization, ③ Speaker embedding quality, ④ Stable SIP/RTC integration, ⑤ Fallback & misinterpretation policies.

1｜Requirements Definition: What Does “Working” Look Like?

1-1. Functional Requirements (MVP)

• Bidirectional calling: Convert incoming English into your Japanese voice, with optional one-way mode.
• Voice matching: Use speaker embedding derived from 30–60 seconds of audio to drive cross-lingual TTS that mimics your voice in English.
• Low-latency streaming: Use partial ASR + chunked TTS for conversational pace.
• Call UI: Toggle mute / translation on/off / language pair, show transcripts, and pin key phrases.
• Safety: Automatic disclaimer at start (e.g. “This call uses real-time translation”). Enforce voice consent and misuse prevention (e.g. impersonation).

1-2. Non-functional Requirements

• Latency: ≤900ms one-way, ≤1.8s round trip.
• Availability: 99.9% uptime during peak hours. Regional failover supported.
• Security: TLS/SRTP encryption, KMS-managed speaker embeddings, principle of least privilege.
• Scalability: 1 call = 1 GPU/CPU thread per microservice.
• Cost-efficiency: Segment long text, synthesize TTS only as needed, apply caching/routing.

2｜System Architecture: Components and Data Flow

[User Device (Mobile/PC)]
   └─ WebRTC (Opus 16k) ─→ [RTC GW / SFU]
                               └─ g.711/Opus Conversion
                                   │
                                   ├─→ [VAD/Segmentation] → [ASR-JA (Streaming)]
                                   │                          │
                                   │                          └→ [Translation JA→EN]
                                   │                                   │
                                   │                                   └→ [TTS-EN (Speaker Embedding)] → [VC (Optional)]
                                   │                                                         │
                                   └─────────────────────────────────────────────────────────┘
                                                             ↓
                                                        [Callee Line]

Key components:

• VAD: Detect silence to segment speech and reduce latency.
• ASR: Streaming, returns partial outputs as audio flows.
• MT: Low-latency translation per segment, supports custom dictionaries.
• TTS: Cross-lingual synthesis using speaker embeddings (e.g., x-vector/ECAPA).
• VC: Optional voice conversion to refine speaker likeness if TTS alone isn’t sufficient.
• RTC/SIP Integration: Bridge between WebRTC (Opus) and SIP/PSTN (G.711) via B2BUA.
• Telemetry: Track latency per module, WER/BLEU, synthesis quality, and drop rates.

3｜Model Selection: Choosing Safely & Effectively

• ASR (JA): Must support streaming, noise robustness, custom lexicons. CPU-optimized versions for low-resource devices.
• MT (JA→EN): Retain proper nouns, control style (e.g., honorifics to polite English), and support domain-specific terminology.
• TTS (EN): Cross-lingual synthesis with speaker embedding support and chunked synthesis APIs.
• VC: Optional, used only with user consent. Avoid over-conversion for realism.
• Safety Strategy: Predefine fallback behaviors for medical/legal/financial topics (e.g., return generic templates or hand off to humans).

Recommendation: Zero-shot multilingual TTS with speaker embeddings offers the best trade-off between simplicity and quality. VC is a secondary enhancement.

4｜Latency Breakdown & Optimization Techniques

• VAD: 80–120ms
• ASR: 150–250ms (streaming with 256–512ms frames)
• MT: 60–120ms (with punctuation-based segmentation)
• TTS: 150–250ms (subword synthesis + just-in-time playback)
• RTC Network: 80–200ms with regional routing + jitter buffers (20–40ms)

Total one-way: ≈600–900ms
<1 sec feels natural for human dialogue.

5｜Data Models & API Design (JSON Schema)

5-1. Speaker Enrollment

POST /v1/speaker/enroll
{
  "user_id": "u_123",
  "consent": true,
  "audio_samples": [
    {"format":"wav","rate":16000,"bytes":"...base64..."}
  ],
  "locale": "ja-JP",
  "notes": "Registering user voice with consent"
}

5-2. Call Start

POST /v1/call/start
{
  "caller_id":"u_123",
  "callee":"+1-xxx",
  "mode":"bidirectional",
  "source_lang":"ja-JP",
  "target_lang":"en-US",
  "speaker_id":"spk_u_123",
  "safety_profile":"default"
}

5-3. Streaming (WebSocket)

• audio.in (PCM/Opus 16k)
• asr.partial / asr.final
• mt.segment
• tts.chunk (PCM stream)
• status (latency/rate/drop metrics)

6｜MVP Pipeline Sample (Python/asyncio)

See original for code — demonstrates VAD → ASR → MT → TTS with streaming yields and buffer management.

Key ideas:

• 20ms frames + silence detection for segmentation.
• Async chaining: TTS of sentence N while ASR of sentence N+1 starts.
• Postfix synthesis polishing after utterance ends improves intonation.

7｜SIP/PSTN Integration: Practical Architecture & Pitfalls

• Use WebRTC client + SFU/RTC GW + B2BUA (e.g., Asterisk) → PSTN
• Translation engine forks audio from bridge, sends mixed TTS to callee.
• Challenges:
  • Echo/double audio → use one-way mix, lower TTS volume, suggest earphones.
  • DTMF/IVR interference → provide TTS pause button.
  • Codec quality loss → adjust EQ/AGC for 16k→8k downsampling.
  • Recording/legal policies must be explicit.

8｜Improving “Voice Likeness” and Quality

• Embedding: Quiet, echo-free samples; include natural traits like chuckles/slurs.
• TTS prompts: Use punctuation/newlines to control prosody.
• VC: Only use when TTS alone lacks fidelity. Avoid excessive conversion.
• Normalize to -16 LUFS, peak ≤ -1 dBFS.
• Warn user to speak slowly if rate exceeds threshold.

9｜Custom Dictionary & Style Control

• Dictionary: Preserve names/terms (e.g., Acme, Tokyo).
• Style Guide:
  • “恐れ入りますが” → “Could you…”
  • Normalize dates/times (e.g., ISO, AM/PM).
• Forbidden Topics: Generalize high-risk content, escalate to human.
• Post-call feedback: Correct errors → auto-update dictionary.

10｜Evaluation Metrics: What to Measure

• ASR: WER (%), goal = ≤10–15% by domain.
• Translation: BLEU/COMET + human scoring. Separate proper noun accuracy.
• TTS: MOS, speaker similarity (cosine), audio artifacts.
• Latency: Median and 95p ≤ 900ms.
• Task success: Completion rates in booking/troubleshooting.
• Safety: Misleading translation rate, escalation response time.

Test Scripts:

• Restaurant booking
• Package tracking
• Name/address spelling

11｜Production Ops: SRE & Cost Strategy

• Split services into ASR, MT, TTS, RTC-bridge pods.
• Scale with 1-call = 1-inference session, warm-pool for startup.
• Route short vs. long text to fast vs. accurate models.
• Cache standard phrases, synthesize once.
• Monitor latency/drop/errors on edge & server.
• Log model name/version/timestamp/dictionary version in every output.
• Fallbacks: use generic TTS if model fails, text input if ASR fails.

12｜Legal, Ethical & Privacy Guidelines

• User consent: Require digital consent + ID check (phrase + live voice/face).
• Impersonation prevention: Limit embedding use to registered users.
• Recording: Define transcript/storage policies explicitly.
• Disclosure: Notify other party with voice prompt at start.
• High-risk calls: Guide users toward text confirmation for final decisions.
• Region laws: Comply with local rules; predefine internal policies.

13｜Deployment in 3 Sprints (2 weeks each)

Sprint 1: MVP with one-way calls
Sprint 2: Add voice synthesis & bidirectionality
Sprint 3: Add safety, UX, monitoring, and optimization

14｜Reusable Prompts & Templates

Call intro (EN)

“Hi, I’m using call translation. You’ll hear my voice in English. Please speak naturally, and I’ll confirm key details.”

Confirmation Template

“Let me confirm: two people at 7 p.m. tomorrow, indoor seating. Is that correct?”

High-risk block message

“I can only provide general information in calls. For medical or legal advice, I’ll connect you to a human agent now.”

Custom Dictionary (JSON)

{
  "keep_as_is": ["Acme Co.", "Shinjuku", "Tōkyō"],
  "ja_to_en": {"御社":"your company", "検収":"acceptance"},
  "date_format":"MMM d, yyyy"
}

15｜Who Should Use This and Why It Matters

• Sales / CS teams: Improve first-call resolution, cut down transfers/callbacks.
• Travelers / students / global families: Keep emotional tone in important calls.
• Healthcare / Education: Use as assistive tool, with text backup for clarity.
• Accessibility: Combine text + translation + voice for inclusive comms.
• IT / SREs: Balanced cost/stability via routing, caching, fallback.

16｜FAQs (Quick Version)

Q. Isn’t registering my voice risky?
A. No, if managed with consent, encryption, and account binding. Must support deletion requests.

Q. What if latency goes too high?
A. Use shorter phrases, lighter models, faster regions, chunked synthesis, dictionaries.

Q. Does translation need to be perfect?
A. No. With confirmation + text follow-up, it’s sufficient for most tasks.

17｜Final Notes: Start with One Calm Sentence

• Great UX comes from flow design, not just fancy models.
• Start one-way, hit <900ms, then expand to two-way.
• Focus on speaker trust, disclosure, and fallback.
• “Speak in Japanese, get heard in English—in your own voice.” That’s real, and it starts with the right architecture.