While transcription completes the input side of the voice pipeline, Text-to-Speech (TTS) is responsible for delivering output that feels natural, expressive, and comprehensible — across languages, form factors, and application contexts. The TTS Rendering Engine in DAKSH is not a generic text reader; it is a structured-response-aware, multilingual vocalization layer designed to preserve meaning, tone, and clarity of structured content such as lists, tables, instructions, FAQs, and policy snippets.
DAKSH’s TTS engine is built as a modular subsystem that supports multiple rendering backends depending on deployment scale, latency requirement, and linguistic needs. These include lightweight libraries like flutter_tts for client-side apps, and neural TTS services (e.g., Coqui, VITS, Tacotron derivatives) for centralized or server-rendered responses. All engines are abstracted under a unified interface that exposes controls for voice selection, speech speed, pitch, and segmentation.
a) Response Structuring for Speech
The first step in the TTS pipeline is formatting the generated response from the LLM into a speakable format. DAKSH uses a response formatter that:
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Converts structured outputs (e.g., JSON or bullet points) into natural phrasing.
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Breaks long paragraphs into manageable clauses using punctuation and syntactic markers.
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Assigns verbal emphasis to keywords using SSML-like markup (e.g., <emphasis> for dates, names, amounts).
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Tags inline segments with custom prosody cues such as slower pace for numeric data or pauses before steps in instructions.
For instance, a JSON output like:
jsonCopyEdit{ "due_date": "15 April 2025", "amount": "₹500", "status": "Pending" }
is rendered as:
“Your payment of ₹500 is pending. The due date is fifteenth April twenty twenty-five.”
This transformation ensures the user receives not just raw information but a spoken summary optimized for comprehension and retention.
b) Language-Specific Voice Selection
DAKSH supports over 15 Indian and global languages for voice output. During TTS invocation, the language of the query is preserved to maintain continuity and comfort. The rendering engine selects a voice model based on:
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User Language Preference (explicit or inferred from query)
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Formality Level of the response (casual vs. official tone)
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Device Capabilities (e.g., limited voice models on mobile vs. cloud)
For example:
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Hindi users may hear a neutral North Indian male or female voice depending on configuration.
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Marathi users may receive a local-accented voice with phrasebook adaptations.
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Bilingual users can toggle between English and regional languages dynamically without reloading the interface.
Each voice model is carefully calibrated to sound natural, intelligible, and culturally appropriate — avoiding robotic or overly synthetic tonality.
c) Dynamic Vocal Rendering Controls
The TTS module supports rich control over how responses are vocalized, including:
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Pitch Modulation: For emphasis or sentiment adaptation (e.g., rising tone for questions).
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Pause Insertion: Automatic breakpoints between list items or topic shifts.
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Rate Adjustment: Faster rendering for familiar phrases; slower for instructions or figures.
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Interrupt Recovery: If the user speaks while the bot is responding, playback is paused or shortened based on intent detection.
In kiosk or IVR deployments, the system also integrates voice bar animation or synchronized subtitles for accessibility.
d) Offline and Edge Voice Support
In mobile or offline-first deployments, DAKSH includes local fallback TTS using embedded libraries. This ensures that even without internet connectivity, users can still receive voice responses for cached or known queries. This mode is essential for rural deployments, citizen service centers, and field-facing applications.
Furthermore, voice synthesis respects privacy — responses are never logged in audio form unless explicitly configured. In most configurations, TTS is generated on-device or via short-lived encrypted streams that self-expire post-delivery.