AI-enhanced speech recognition in XR

1. Core System Architecture

A. Multi-Modal Input Pipeline

graph LR
    A[Microphone Array] --> B[Beamforming]
    B --> C[Speech Enhancement]
    C --> D[ASR Engine]
    D --> E[Intent Recognition]
    E --> F[XR Action System]
    G[Head Movement] --> H[Context Weighting]
    H --> E

**B. Platform-Specific ASR Options

Platform	Recommended Engine	Latency	Vocabulary
Meta Quest	Meta Voice SDK	300ms	50k words
HoloLens 2	Windows Speech RT	250ms	100k words
Apple Vision Pro	Siri Speech Framework	200ms	Unlimited*
Custom Solutions	Whisper.cpp (On-device)	500ms	Multilingual

*With cloud fallback

2. Key Enhancement Techniques

A. Noise-Robust Processing

# Python pseudo-code for audio enhancement
def enhance_audio(audio_clip):
    # Spectral subtraction
    enhanced = nr.reduce_noise(
        y=audio_clip, 
        sr=16000,
        stationary=True,
        prop_decrease=0.85
    )

    # XR-specific voice isolation
    if xr_context.hmd_type == "Quest":
        enhanced = apply_quest_voice_filter(enhanced)

    return enhanced

B. Spatial Voice Recognition

// Unity implementation for directional ASR
public class DirectionalASR : MonoBehaviour
{
    void Update()
    {
        if (OVRInput.GetVoiceDirection(out Vector3 dir))
        {
            currentSpeaker = FindNearestAvatar(dir);
            asrEngine.SetSpeakerProfile(currentSpeaker.voiceProfile);
        }
    }
}

3. Performance Optimization

A. Real-Time Constraints

Parameter	VR Threshold	AR Threshold
End-to-End Latency	<500ms	<300ms
Wake Word Detection	<100ms	<50ms
False Accept Rate	<0.1%	<0.01%

**B. Hardware Acceleration

• Quest 3: Hexagon DSP for always-on wake word
• Vision Pro: Neural Engine for on-device Whisper
• Enterprise AR: NVIDIA Riva on edge servers

4. Context-Aware Features

A. Environment-Adaptive Models

# Dynamic model selection
def select_asr_model(env_type):
    if env_type == "industrial":
        return load_model("asr_industrial.onnx")
    elif env_type == "medical":
        return load_model("asr_medical.onnx")
    else:
        return load_model("asr_general.onnx")

B. Gaze-Weighted Recognition

// Unity example for attention-based ASR
float CalculateConfidence(Vector3 gazeDir, Vector3 soundDir)
{
    float angle = Vector3.Angle(gazeDir, soundDir);
    return Mathf.Clamp01(1 - angle/90f); // 0-1 confidence
}

5. Advanced Implementation

A. Multi-Language Code-Switching

graph TD
    A[Audio Input] --> B{Language Detection}
    B -->|English| C[EN ASR]
    B -->|Spanish| D[ES ASR]
    C & D --> E[Unified NLU]

B. Emotion Recognition Integration

def analyze_speech(audio):
    text = asr.transcribe(audio)
    emotion = emotion_classifier(audio)

    return {
        "text": text,
        "emotion": emotion,
        "urgency": 0.8 if emotion == "angry" else 0.2
    }

6. Emerging Technologies

• Neural Voice Codecs (3x bandwidth reduction)
• Lip Movement Synthesis from audio (for avatars)
• EEG-assisted ASR (silent speech interfaces)

Debugging Toolkit

// Unity Real-Time ASR Monitor
public class ASRDebugger : MonoBehaviour
{
    void OnASRResult(string transcript, float confidence)
    {
        debugText.text = $"<color={GetColor(confidence)}>{transcript}</color>";
        xrDebugPanel.Log($"ASR: {transcript} ({confidence:P0})");
    }
}

Implementation Checklist:
✔ Select platform-optimized ASR backend
✔ Implement environmental noise profiles
✔ Add spatial voice weighting
✔ Design fallback mechanisms
✔ Profile thermal/power impact