Voice Mode Enhancement - 10 Phase Implementation

Status: ✅ COMPLETE (2025-12-03) All 10 phases implemented with full backend-frontend integration

This document describes the comprehensive 10-phase enhancement to VoiceAssist's voice mode, transforming it from a functional voice assistant into a human-like conversational partner with medical dictation capabilities.

Executive Summary

Primary Goals Achieved:

1. ✅ Natural, human-like voice interactions
2. ✅ Contextual memory across conversations
3. ✅ Professional medical dictation
4. ✅ Natural backchanneling
5. ✅ Session analytics and feedback collection

Key External Services:

• Hume AI - Emotion detection from audio (HIPAA BAA available)
• Deepgram Nova-3 Medical - Upgraded STT for medical vocabulary
• ElevenLabs - TTS with backchanneling support

Phase Implementation Status

Architecture Overview

┌─────────────────────────────────────────────────────────────────────────────┐
│                        ENHANCED VOICE PIPELINE                               │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   User Audio ──┬──> Deepgram Nova-3 ──> Transcript ──┐                      │
│                │    (Medical STT)                     │                      │
│                │                                      │                      │
│                ├──> Hume AI ──────────> Emotion ──────┼──> Context Builder  │
│                │    (Emotion)                         │                      │
│                │                                      │                      │
│                └──> Prosody Analyzer ──> Urgency ─────┘                      │
│                     (from Deepgram)                                          │
│                                                                              │
│   Context Builder ──┬──> Short-term (Redis) ─────────┐                      │
│                     ├──> Medium-term (PostgreSQL) ───┼──> Memory Service    │
│                     └──> Long-term (Qdrant vectors) ─┘                      │
│                                                                              │
│   Memory + Emotion + Transcript ──> Thinker (GPT-4o) ──> Response           │
│                                                                              │
│   Response ──> Turn Manager ──> TTS (ElevenLabs) ──> User                   │
│                    │                                                         │
│                    └──> Backchannel Service (parallel audio)                │
│                                                                              │
│   Session Analytics ──> Metrics + Latency Tracking ──> Feedback Prompts     │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Phase 1: Emotional Intelligence

Goal: Detect user emotions from speech and adapt responses accordingly.

Backend Service

Location: services/api-gateway/app/services/emotion_detection_service.py

class EmotionDetectionService:
    """
    Wraps Hume AI Expression Measurement API.
    - Analyzes audio chunks (500ms) in parallel with STT
    - Returns: valence, arousal, discrete emotions
    - Caches recent emotion states for trending
    """

    async def analyze_audio_chunk(self, audio: bytes) -> EmotionResult
    async def get_emotion_trend(self, session_id: str) -> EmotionTrend
    def map_emotion_to_response_style(self, emotion: str) -> VoiceStyle

WebSocket Message

{ type: "emotion.detected", data: { emotion: string, confidence: number, valence: number, arousal: number } }

Frontend Handler

In useThinkerTalkerSession.ts:

onEmotionDetected?: (event: TTEmotionDetectedEvent) => void;

Latency Impact: +50-100ms (parallel, non-blocking)

Phase 2: Backchanneling System

Goal: Natural verbal acknowledgments during user speech.

Backend Service

Location: services/api-gateway/app/services/backchannel_service.py

class BackchannelService:
    """
    Generates and manages backchanneling audio.
    - Pre-caches common phrases per voice
    - Triggers based on VAD pause detection
    """

    PHRASES = {
        "en": ["uh-huh", "mm-hmm", "I see", "right", "got it"],
        "ar": ["اها", "نعم", "صح"]
    }

    async def get_backchannel_audio(self, phrase: str, voice_id: str) -> bytes
    def should_trigger(self, session_state: SessionState) -> bool

Timing Logic

• Trigger after 2-3 seconds of continuous user speech
• Only during natural pauses (150-300ms silence)
• Minimum 5 seconds between backchannels
• Never interrupt mid-sentence

WebSocket Message

{ type: "backchannel.trigger", data: { phrase: string, audio_base64: string } }

Latency Impact: ~0ms (pre-cached audio)

Phase 3: Prosody Analysis

Goal: Analyze speech patterns for better intent understanding.

Backend Service

Location: services/api-gateway/app/services/prosody_analysis_service.py

@dataclass
class ProsodyAnalysis:
    speech_rate_wpm: float      # Words per minute
    pitch_variance: float       # Emotion indicator
    loudness: float             # Urgency indicator
    pause_patterns: List[float] # Hesitation detection
    urgency_score: float        # Derived 0-1 score
    confidence_score: float     # Speaker certainty

Integration

• Parses Deepgram's prosody/topics metadata
• Matches response speech rate to user's rate
• Detects uncertainty from pitch patterns

Latency Impact: +0ms (data from Deepgram)

Phase 4: Memory & Context System

Goal: Conversation memory across turns and sessions.

Backend Service

Location: services/api-gateway/app/services/memory_context_service.py

class MemoryContextService:
    """Three-tier memory management."""

    async def store_turn_context(self, user_id, session_id, turn) -> None
        # Redis: last 10 turns, TTL = session duration

    async def get_recent_context(self, user_id, session_id, turns=5) -> list
        # Retrieve from Redis

    async def summarize_session(self, session_id) -> SessionContext
        # LLM-generated summary at session end

    async def store_long_term_memory(self, user_id, memory) -> str
        # Store in PostgreSQL + Qdrant vector

    async def retrieve_relevant_memories(self, user_id, query, top_k=5) -> list
        # Semantic search over Qdrant

    async def build_context_window(self, user_id, session_id, query) -> str
        # Assemble optimized context for LLM (max 4K tokens)

WebSocket Message

{ type: "memory.context_loaded", data: { memories: Memory[], relevance_scores: number[] } }

Phase 5 & 6: Turn-Taking and Response Timing

Goal: Fluid conversation flow with natural turn transitions and human-like timing.

Turn States

class TurnTakingState(Enum):
    USER_TURN = "user_turn"
    TRANSITION = "transition"   # Brief transition window
    AI_TURN = "ai_turn"
    OVERLAP = "overlap"         # Both speaking (barge-in)

Response Timing Configuration

RESPONSE_TIMING = {
    "urgent": {"delay_ms": 0, "use_filler": False},      # Medical emergency
    "simple": {"delay_ms": 200, "use_filler": False},    # Yes/no, confirmations
    "complex": {"delay_ms": 600, "use_filler": True},    # Multi-part questions
    "clarification": {"delay_ms": 0, "use_filler": False}
}

WebSocket Message

{ type: "turn.state", data: { state: "user_turn" | "transition" | "ai_turn" } }

Phase 7: Conversational Repair

Goal: Graceful handling of misunderstandings.

Backend Service

Location: services/api-gateway/app/services/repair_strategy_service.py

class RepairStrategy(Enum):
    ECHO_CHECK = "echo_check"           # "So you're asking about X?"
    CLARIFY_SPECIFIC = "clarify_specific"  # "Did you mean X or Y?"
    REQUEST_REPHRASE = "request_rephrase"  # "Could you say that differently?"
    PARTIAL_ANSWER = "partial_answer"      # "I'm not sure, but..."

Features

• Confidence scoring for responses
• Clarifying questions when confidence < 0.7
• Natural upward inflection for questions (SSML)
• Frustration detection from repeated corrections

Phase 8: Medical Dictation Core

Goal: Hands-free clinical documentation.

Backend Services

Location: services/api-gateway/app/services/

dictation_service.py

class DictationState(Enum):
    IDLE = "idle"
    LISTENING = "listening"
    PROCESSING = "processing"
    PAUSED = "paused"
    REVIEWING = "reviewing"

class NoteType(Enum):
    SOAP = "soap"           # Subjective, Objective, Assessment, Plan
    HP = "h_and_p"          # History and Physical
    PROGRESS = "progress"   # Progress Note
    PROCEDURE = "procedure"
    CUSTOM = "custom"

voice_command_service.py

# Navigation
"go to subjective", "move to objective", "next section", "previous section"

# Formatting
"new paragraph", "bullet point", "number one/two/three"

# Editing
"delete that", "scratch that", "read that back", "undo"

# Clinical
"check interactions", "what's the dosing for", "show labs", "show medications"

# Control
"start dictation", "pause", "stop dictation", "save note"

note_formatter_service.py

• LLM-assisted note formatting
• Grammar correction preserving medical terminology
• Auto-punctuation and abbreviation handling

medical_vocabulary_service.py

• Specialty-specific keyword sets
• User-customizable vocabulary
• Medical abbreviation expansion

WebSocket Messages

{ type: "dictation.state", data: { state: DictationState, note_type: NoteType } }
{ type: "dictation.section_update", data: { section: string, content: string } }
{ type: "dictation.section_change", data: { previous: string, current: string } }
{ type: "dictation.command", data: { command: string, executed: boolean } }

Phase 9: Patient Context Integration

Goal: Context-aware clinical assistance with HIPAA compliance.

Backend Services

patient_context_service.py

class PatientContextService:
    async def get_context_for_dictation(self, user_id, patient_id) -> DictationContext
    def generate_context_prompts(self, context) -> List[str]
        # "I see 3 recent lab results. Would you like me to summarize them?"

dictation_phi_monitor.py

• Real-time PHI detection during dictation
• Alert if unexpected PHI spoken outside patient context

HIPAA Audit Events

# Added to audit_service.py
DICTATION_STARTED = "dictation_started"
PATIENT_CONTEXT_ACCESSED = "patient_context_accessed"
NOTE_SAVED = "note_saved"
PHI_DETECTED = "phi_detected"

WebSocket Messages

{ type: "patient.context_loaded", data: { patientId: string, context: PatientContext } }
{ type: "phi.alert", data: { severity: string, message: string, detected_phi: string[] } }

Phase 10: Frontend Integration & Analytics

Goal: Session analytics, feedback collection, and full frontend integration.

Backend Services

session_analytics_service.py

Location: services/api-gateway/app/services/session_analytics_service.py

class SessionAnalyticsService:
    """
    Comprehensive voice session analytics tracking.

    Tracks:
    - Latency metrics (STT, LLM, TTS, E2E) with percentiles
    - Interaction counts (utterances, responses, tool calls, barge-ins)
    - Quality metrics (confidence scores, turn-taking, repairs)
    - Dictation-specific metrics
    """

    def create_session(self, session_id: str, user_id: Optional[str], mode: str,
                       on_analytics_update: Optional[Callable]) -> SessionAnalytics

    def record_latency(self, session_id: str, latency_type: str, latency_ms: float) -> None
    def record_interaction(self, session_id: str, interaction_type: InteractionType,
                          word_count: int, duration_ms: float) -> None
    def record_emotion(self, session_id: str, emotion: str, valence: float, arousal: float) -> None
    def record_barge_in(self, session_id: str) -> None
    def record_repair(self, session_id: str) -> None
    def record_error(self, session_id: str, error_type: str, message: str) -> None

    def end_session(self, session_id: str) -> Optional[Dict[str, Any]]

feedback_service.py

Location: services/api-gateway/app/services/feedback_service.py

class FeedbackService:
    """
    User feedback collection for voice sessions.

    Features:
    - Quick thumbs up/down during session
    - Detailed session ratings with categories
    - Bug reports and suggestions
    - Feedback prompts based on session context
    """

    def record_quick_feedback(self, session_id: str, user_id: Optional[str] = None,
                              thumbs_up: bool = True, message_id: Optional[str] = None) -> FeedbackItem

    def record_session_rating(self, session_id: str, user_id: Optional[str] = None,
                              rating: int = 5, categories: Optional[Dict[str, int]] = None,
                              comment: Optional[str] = None) -> List[FeedbackItem]

    def get_feedback_prompts(self, session_id: str, session_duration_ms: float = 0,
                            interaction_count: int = 0, has_errors: bool = False) -> List[FeedbackPrompt]

    def generate_analytics_report(self, session_ids: Optional[List[str]] = None) -> Dict[str, Any]

Analytics Data Structure

interface TTSessionAnalytics {
  sessionId: string;
  userId: string | null;
  phase: string;
  mode: string;
  timing: {
    startedAt: string;
    endedAt: string | null;
    durationMs: number;
  };
  latency: {
    stt: { count: number; total: number; min: number; max: number; p50: number; p95: number; p99: number };
    llm: { count: number; total: number; min: number; max: number; p50: number; p95: number; p99: number };
    tts: { count: number; total: number; min: number; max: number; p50: number; p95: number; p99: number };
    e2e: { count: number; total: number; min: number; max: number; p50: number; p95: number; p99: number };
  };
  interactions: {
    counts: Record<string, number>;
    words: { user: number; assistant: number };
    speakingTimeMs: { user: number; assistant: number };
  };
  quality: {
    sttConfidence: { count: number; total: number; min: number; max: number };
    aiConfidence: { count: number; total: number; min: number; max: number };
    emotion: { dominant: string | null; valence: number; arousal: number };
    turnTaking: { bargeIns: number; overlaps: number; smoothTransitions: number };
    repairs: number;
  };
  dictation: {
    sectionsEdited: string[];
    commandsExecuted: number;
    wordsTranscribed: number;
  } | null;
  errors: {
    count: number;
    details: Array<{ timestamp: string; type: string; message: string }>;
  };
}

WebSocket Messages

// Analytics
{ type: "analytics.update", data: TTSessionAnalytics }
{ type: "analytics.session_ended", data: TTSessionAnalytics }

// Feedback
{ type: "feedback.prompts", data: { prompts: TTFeedbackPrompt[] } }
{ type: "feedback.recorded", data: { thumbsUp: boolean, messageId: string | null } }

Frontend Handlers

In useThinkerTalkerSession.ts:

// Phase 10 callbacks
onAnalyticsUpdate?: (analytics: TTSessionAnalytics) => void;
onSessionEnded?: (analytics: TTSessionAnalytics) => void;
onFeedbackPrompts?: (event: TTFeedbackPromptsEvent) => void;
onFeedbackRecorded?: (event: TTFeedbackRecordedEvent) => void;

Complete WebSocket Protocol

All Message Types

Integration Points

Voice Pipeline Service

Location: services/api-gateway/app/services/voice_pipeline_service.py

The voice pipeline service orchestrates all 10 phases:

class VoicePipelineService:
    # Phase 1-9 services
    _emotion_detector: EmotionDetectionService
    _backchannel_service: BackchannelService
    _prosody_analyzer: ProsodyAnalysisService
    _memory_service: MemoryContextService
    _repair_service: RepairStrategyService
    _dictation_service: DictationService
    _voice_command_service: VoiceCommandService
    _note_formatter: NoteFormatterService
    _medical_vocabulary: MedicalVocabularyService
    _patient_context_service: PatientContextService
    _phi_monitor: DictationPHIMonitor

    # Phase 10 services
    _analytics: SessionAnalytics
    _analytics_service: SessionAnalyticsService
    _feedback_service: FeedbackService

    async def start(self):
        # Initialize analytics session
        self._analytics = self._analytics_service.create_session(
            session_id=self.session_id,
            user_id=self.user_id,
            mode="dictation" if self.config.mode == PipelineMode.DICTATION else "conversation",
            on_analytics_update=self._send_analytics_update,
        )

    async def stop(self):
        # Send feedback prompts
        prompts = self._feedback_service.get_feedback_prompts(...)
        await self._on_message(PipelineMessage(type="feedback.prompts", ...))

        # Finalize analytics
        final_analytics = self._analytics_service.end_session(self.session_id)
        await self._on_message(PipelineMessage(type="analytics.session_ended", ...))

Frontend Hook

Location: apps/web-app/src/hooks/useThinkerTalkerSession.ts

All 10 phases integrated with callbacks:

export interface UseThinkerTalkerSessionOptions {
  // ... existing options ...

  // Phase 1: Emotion
  onEmotionDetected?: (event: TTEmotionDetectedEvent) => void;

  // Phase 2: Backchanneling
  onBackchannelTrigger?: (event: TTBackchannelTriggerEvent) => void;

  // Phase 4: Memory
  onMemoryContextLoaded?: (event: TTMemoryContextLoadedEvent) => void;

  // Phase 5: Turn-taking
  onTurnStateChange?: (event: TTTurnStateChangeEvent) => void;

  // Phase 8: Dictation
  onDictationStateChange?: (event: TTDictationStateChangeEvent) => void;
  onDictationSectionUpdate?: (event: TTDictationSectionUpdateEvent) => void;
  onDictationSectionChange?: (event: TTDictationSectionChangeEvent) => void;
  onDictationCommand?: (event: TTDictationCommandEvent) => void;

  // Phase 9: Patient Context
  onPatientContextLoaded?: (event: TTPatientContextLoadedEvent) => void;
  onPHIAlert?: (event: TTPHIAlertEvent) => void;

  // Phase 10: Analytics & Feedback
  onAnalyticsUpdate?: (analytics: TTSessionAnalytics) => void;
  onSessionEnded?: (analytics: TTSessionAnalytics) => void;
  onFeedbackPrompts?: (event: TTFeedbackPromptsEvent) => void;
  onFeedbackRecorded?: (event: TTFeedbackRecordedEvent) => void;
}

File Reference

Backend Services (New)

Backend Services (Modified)

Frontend

Success Metrics

Related Documentation

• VOICE_MODE_PIPELINE.md - Core pipeline architecture
• VOICE_MODE_SETTINGS_GUIDE.md - User settings
• VOICE_STATE_2025-11-29.md - Voice state snapshot

Last updated: 2025-12-03 All 10 phases implemented and integrated