KI_Voicebot_Architektur:
  Input_Layer:
    - Mikrofonaufnahme (Web/Telefon/App)
    - Rauschunterdrückung
    - Audio-Preprocessing

  Speech_Recognition:
    - Azure Speech Services
    - Google Cloud Speech-to-Text
    - Custom Acoustic Models

  NLP_Engine:
    - Intent Recognition
    - Entity Extraction
    - Context Management
    - Sentiment Analysis

  Business_Logic:
    - CRM/ERP Integration
    - Knowledge Base Access
    - Workflow Automation
    - Decision Trees

  Response_Generation:
    - LLM-powered Responses
    - Template-based Answers
    - Dynamic Content

  Voice_Synthesis:
    - Neural Text-to-Speech
    - Custom Voice Models
    - Emotion & Tone Control

# KI Voicebot für Kundenservice
class CustomerServiceVoicebot:
    def __init__(self, azure_config):
        self.speech_service = AzureSpeechService(azure_config)
        self.nlp_engine = AzureLanguageService(azure_config)
        self.crm_connector = CRMConnector()

    def handle_customer_call(self, audio_stream, customer_id=None):
        """
        Verarbeitung von Kundenanrufen mit Voice AI
        """
        # 1. Spracherkennung
        transcript = self.speech_service.recognize_speech(audio_stream)

        # 2. Intent & Entity Erkennung
        intent_result = self.nlp_engine.analyze_intent(transcript)

        # 3. Kundenkontext abrufen
        if customer_id:
            customer_context = self.crm_connector.get_customer_data(customer_id)
        else:
            customer_context = self.identify_customer(transcript)

        # 4. Antwort generieren
        response = self.generate_response(
            intent=intent_result.intent,
            entities=intent_result.entities,
            customer_context=customer_context,
            conversation_history=self.get_conversation_history()
        )

        # 5. Sprachsynthese
        audio_response = self.speech_service.synthesize_speech(
            text=response,
            voice_style="professional",
            emotion="friendly"
        )

        return {
            'transcript': transcript,
            'intent': intent_result.intent,
            'response_text': response,
            'audio_response': audio_response,
            'confidence': intent_result.confidence
        }

# Voice Commerce Implementation
class VoiceCommerceBot:
    def __init__(self):
        self.product_catalog = ProductCatalogService()
        self.order_system = OrderManagementSystem()

    def process_voice_order(self, speech_input, customer_profile):
        """
        Sprachbasierte Bestellabwicklung
        """
        # Produkt-Intent erkennen
        product_request = self.extract_product_intent(speech_input)

        if product_request['intent'] == 'place_order':
            # Produktsuche
            products = self.product_catalog.search(
                query=product_request['product_name'],
                filters=product_request['specifications']
            )

            # Bestellbestätigung
            if len(products) == 1:
                order_summary = self.create_order_summary(
                    product=products[0],
                    quantity=product_request['quantity'],
                    customer=customer_profile
                )

                confirmation_text = f"""
                Perfekt! Ich habe {product_request['quantity']}
                {products[0]['name']} für {products[0]['price']}€
                in Ihren Warenkorb gelegt.

                Soll ich die Bestellung abschließen?
                """

                return {
                    'action': 'order_confirmation',
                    'order_summary': order_summary,
                    'response': confirmation_text,
                    'next_step': 'await_confirmation'
                }

        return self.handle_complex_request(speech_input)

# Enterprise Voice Assistant
class EnterpriseVoiceAssistant:
    def __init__(self):
        self.employee_directory = EmployeeDirectory()
        self.calendar_service = CalendarService()
        self.document_search = DocumentSearchService()

    def handle_employee_request(self, voice_input, employee_context):
        """
        Bearbeitung von Mitarbeiteranfragen
        """
        intent = self.classify_employee_intent(voice_input)

        if intent == 'schedule_meeting':
            return self.schedule_meeting_by_voice(voice_input, employee_context)

        elif intent == 'find_document':
            return self.search_documents_by_voice(voice_input, employee_context)

        elif intent == 'employee_lookup':
            return self.find_employee_info(voice_input)

        elif intent == 'report_request':
            return self.generate_voice_report(voice_input, employee_context)

    def schedule_meeting_by_voice(self, voice_input, employee):
        """
        Sprachbasierte Terminplanung
        """
        meeting_details = self.extract_meeting_details(voice_input)

        # Verfügbarkeit prüfen
        availability = self.calendar_service.check_availability(
            attendees=meeting_details['attendees'],
            preferred_time=meeting_details['time'],
            duration=meeting_details['duration']
        )

        if availability['is_available']:
            # Meeting erstellen
            meeting = self.calendar_service.create_meeting(
                title=meeting_details['title'],
                attendees=meeting_details['attendees'],
                time=availability['suggested_time'],
                duration=meeting_details['duration']
            )

            response = f"""
            Perfekt! Ich habe das Meeting '{meeting_details['title']}'
            für {availability['suggested_time']} geplant.
            Alle Teilnehmer erhalten eine Einladung.
            """
        else:
            response = f"""
            Leider sind nicht alle Teilnehmer zu diesem Zeitpunkt verfügbar.
            Ich schlage folgende Alternativen vor:
            {', '.join(availability['alternatives'])}
            """

        return {'response': response, 'meeting_created': availability['is_available']}

# Azure Speech Services für deutsche Unternehmen
import azure.cognitiveservices.speech as speechsdk

class AzureVoiceAI:
    def __init__(self, subscription_key, region='westeurope'):
        self.speech_config = speechsdk.SpeechConfig(
            subscription=subscription_key,
            region=region
        )
        # Deutsche Sprachmodelle
        self.speech_config.speech_recognition_language = "de-DE"
        self.speech_config.speech_synthesis_language = "de-DE"

        # Custom Voice Model (optional)
        self.speech_config.endpoint_id = "your-custom-model-id"

    def setup_real_time_recognition(self):
        """
        Echtzeit-Spracherkennung für Live-Gespräche
        """
        audio_config = speechsdk.audio.AudioConfig(use_default_microphone=True)

        self.speech_recognizer = speechsdk.SpeechRecognizer(
            speech_config=self.speech_config,
            audio_config=audio_config
        )

        # Event-Handler für kontinuierliche Erkennung
        self.speech_recognizer.recognizing.connect(self.on_recognizing)
        self.speech_recognizer.recognized.connect(self.on_recognized)
        self.speech_recognizer.session_stopped.connect(self.on_session_stopped)

    def on_recognizing(self, evt):
        """Zwischenergebnisse der Spracherkennung"""
        print(f"Erkenne: {evt.result.text}")

    def on_recognized(self, evt):
        """Finales Erkennungsergebnis"""
        if evt.result.reason == speechsdk.ResultReason.RecognizedSpeech:
            self.process_recognized_speech(evt.result.text)

    def create_custom_voice(self, voice_samples):
        """
        Erstellung einer Custom Voice für Marken-Identität
        """
        # Training einer unternehmensspezifischen Stimme
        voice_model = self.train_custom_voice(
            training_data=voice_samples,
            voice_name="UnternehmenAssistent",
            language="de-DE"
        )

        return voice_model

# Mehrsprachige Voice AI für internationale Unternehmen
class MultilingualVoicebot:
    def __init__(self):
        self.supported_languages = {
            'de-DE': {'voice': 'KatjaNeural', 'model': 'german_model'},
            'en-US': {'voice': 'JennyNeural', 'model': 'english_model'},
            'fr-FR': {'voice': 'DeniseNeural', 'model': 'french_model'},
            'es-ES': {'voice': 'ElviraNeural', 'model': 'spanish_model'}
        }

    def detect_language(self, audio_input):
        """
        Automatische Spracherkennung
        """
        language_detector = LanguageDetector()
        detected_language = language_detector.detect(audio_input)

        return detected_language

    def process_multilingual_request(self, audio_input):
        """
        Verarbeitung mehrsprachiger Anfragen
        """
        # Sprache erkennen
        language = self.detect_language(audio_input)

        # Entsprechendes Modell laden
        model_config = self.supported_languages[language]

        # Spracherkennung in der erkannten Sprache
        transcript = self.recognize_speech(audio_input, language)

        # Antwort in der gleichen Sprache generieren
        response = self.generate_response(transcript, language)

        # Sprachsynthese mit passender Stimme
        audio_response = self.synthesize_speech(
            text=response,
            language=language,
            voice=model_config['voice']
        )

        return {
            'detected_language': language,
            'transcript': transcript,
            'response': response,
            'audio': audio_response
        }

Voice_AI_ROI_Berechnung:
  Kosteneinsparungen:
    Personalkosten_Kundenservice: '€25.000-45.000/Jahr'
    Telefonkosten_Reduzierung: '€5.000-12.000/Jahr'
    Prozessautomatisierung: '€15.000-30.000/Jahr'
    Fehlerreduzierung: '€8.000-15.000/Jahr'

  Umsatzsteigerungen:
    24/7_Verfügbarkeit: '€20.000-50.000/Jahr'
    Voice_Commerce: '€30.000-100.000/Jahr'
    Kundenzufriedenheit: '€10.000-25.000/Jahr'
    Neue_Vertriebskanäle: '€15.000-40.000/Jahr'

  Implementierungskosten:
    Initial_Setup: '€20.000-50.000'
    Custom_Voice_Training: '€5.000-15.000'
    Integration_Kosten: '€10.000-25.000'
    Laufende_Kosten_monatlich: '€1.500-4.000'

  Erwarteter_ROI: '150-300% nach 18 Monaten'

# Privacy-compliant Voice Processing
class PrivacyCompliantVoiceAI:
    def __init__(self):
        self.encryption_service = EncryptionService()
        self.consent_manager = ConsentManager()
        self.audit_logger = AuditLogger()

    def process_voice_with_privacy(self, audio_data, user_consent):
        """
        DSGVO-konforme Sprachverarbeitung
        """
        # 1. Consent-Prüfung
        if not self.consent_manager.has_valid_consent(user_consent, 'voice_processing'):
            return {'error': 'Keine gültige Einverständniserklärung'}

        # 2. Audio-Verschlüsselung
        encrypted_audio = self.encryption_service.encrypt_audio(audio_data)

        # 3. Sprachverarbeitung in sicherer Umgebung
        transcript = self.secure_speech_recognition(encrypted_audio)

        # 4. PII-Entfernung aus Transcript
        anonymized_transcript = self.remove_pii(transcript)

        # 5. Audit-Logging
        self.audit_logger.log_voice_processing({
            'timestamp': datetime.now(),
            'user_id': user_consent['user_id'],
            'processing_type': 'speech_recognition',
            'data_anonymized': True,
            'consent_valid': True
        })

        # 6. Automatische Löschung nach Aufbewahrungszeit
        self.schedule_deletion(encrypted_audio, retention_days=30)

        return {
            'transcript': anonymized_transcript,
            'processing_compliant': True,
            'retention_policy_applied': True
        }

    def implement_voice_consent_management(self):
        """
        Einverständniserklärung für Voice Processing
        """
        consent_flow = {
            'voice_recording_consent': 'Dürfen wir Ihre Sprache aufzeichnen?',
            'speech_analysis_consent': 'Dürfen wir Ihre Sprache analysieren?',
            'voice_storage_consent': 'Dürfen wir Sprachdaten temporär speichern?',
            'quality_improvement_consent': 'Nutzen für Qualitätsverbesserung?'
        }

        return consent_flow

# DSGVO-konforme Voice AI Architektur
EU_Voice_AI_Architecture:
  Data_Processing:
    Location: 'EU-West (Niederlande/Deutschland)'
    Encryption: 'AES-256 in transit & at rest'
    Access_Control: 'Zero-Trust Architecture'

  Speech_Services:
    Provider: 'Azure Speech Services EU'
    Data_Residency: 'EU-only'
    Compliance: 'ISO 27001, SOC 2, GDPR'

  Storage:
    Voice_Data: 'Encrypted EU Storage'
    Retention: 'Configurable (7-365 days)'
    Deletion: 'Automated after retention period'

  Monitoring:
    Audit_Logs: 'Full conversation audit trail'
    Compliance_Monitoring: 'Real-time GDPR compliance'
    Data_Subject_Rights: 'Automated fulfillment'

# Telefonsystem-Integration für Voice AI
class TelephonyVoiceAI:
    def __init__(self, twilio_config):
        self.twilio_client = TwilioClient(twilio_config)
        self.voice_ai = VoiceAIEngine()

    def setup_voice_ivr(self):
        """
        Intelligentes IVR mit Voice AI
        """
        ivr_flow = {
            'welcome_message': self.create_welcome_prompt(),
            'speech_recognition': self.enable_speech_input(),
            'intent_routing': self.setup_intent_routing(),
            'human_escalation': self.setup_escalation_rules()
        }

        return ivr_flow

    def handle_incoming_call(self, call_sid, caller_number):
        """
        Behandlung eingehender Anrufe mit Voice AI
        """
        # Anrufkontext erstellen
        call_context = {
            'caller_number': caller_number,
            'call_time': datetime.now(),
            'previous_interactions': self.get_caller_history(caller_number)
        }

        # Personalisierte Begrüßung
        if call_context['previous_interactions']:
            greeting = f"Willkommen zurück! Wie kann ich Ihnen heute helfen?"
        else:
            greeting = f"Herzlich willkommen! Ich bin Ihr KI-Assistent. Wie kann ich helfen?"

        # Voice AI Dialog starten
        return self.start_voice_conversation(call_context, greeting)

# Omnichannel Voice AI Platform
class OmnichannelVoiceAI:
    def __init__(self):
        self.channels = {
            'phone': TelephonyVoiceAI(),
            'web': WebVoiceInterface(),
            'mobile_app': MobileVoiceSDK(),
            'smart_speaker': AlexaSkillInterface(),
            'teams': MicrosoftTeamsBot()
        }

    def unified_voice_experience(self, user_id, channel, voice_input):
        """
        Einheitliche Voice Experience über alle Kanäle
        """
        # Benutzerkontext laden
        user_context = self.load_user_context(user_id)

        # Channel-spezifische Verarbeitung
        channel_handler = self.channels[channel]
        response = channel_handler.process_voice_input(voice_input, user_context)

        # Conversation State synchronisieren
        self.sync_conversation_state(user_id, response)

        # Antwort für jeweiligen Kanal optimieren
        return self.optimize_response_for_channel(response, channel)

# Voice AI Analytics Dashboard
class VoiceAIAnalytics:
    def __init__(self):
        self.analytics_db = AnalyticsDatabase()

    def track_voice_interactions(self, interaction_data):
        """
        Tracking von Voice Interactions für Optimierung
        """
        metrics = {
            'conversation_id': interaction_data['id'],
            'duration_seconds': interaction_data['duration'],
            'intent_accuracy': interaction_data['intent_confidence'],
            'user_satisfaction': interaction_data['satisfaction_score'],
            'resolution_rate': interaction_data['resolved'],
            'escalation_needed': interaction_data['escalated_to_human'],
            'language_detected': interaction_data['language'],
            'error_count': len(interaction_data['errors'])
        }

        self.analytics_db.store_metrics(metrics)

    def generate_voice_ai_insights(self):
        """
        Voice AI Performance Insights
        """
        insights = {
            'top_intents': self.get_most_common_intents(),
            'accuracy_trends': self.analyze_accuracy_trends(),
            'user_satisfaction': self.measure_satisfaction_trends(),
            'peak_usage_times': self.identify_peak_times(),
            'language_distribution': self.analyze_language_usage(),
            'escalation_patterns': self.analyze_escalation_reasons()
        }

        return insights

    def voice_ai_optimization_recommendations(self):
        """
        Automatische Optimierungsempfehlungen
        """
        recommendations = []

        # Intent-Modell Verbesserungen
        low_confidence_intents = self.find_low_confidence_intents()
        if low_confidence_intents:
            recommendations.append({
                'type': 'intent_training',
                'priority': 'high',
                'description': f'Retraining needed for: {low_confidence_intents}'
            })

        # Voice Quality Verbesserungen
        speech_errors = self.analyze_speech_recognition_errors()
        if speech_errors['error_rate'] > 0.05:
            recommendations.append({
                'type': 'acoustic_model',
                'priority': 'medium',
                'description': 'Consider custom acoustic model training'
            })

        return recommendations

# Healthcare Voice Assistant (DSGVO & Medizinproduktegesetz-konform)
class HealthcareVoiceAI:
    def __init__(self):
        self.medical_knowledge = MedicalKnowledgeBase()
        self.appointment_system = AppointmentManagement()

    def handle_medical_inquiry(self, voice_input, patient_context):
        """
        Medizinische Anfragen mit Voice AI
        """
        # Medizinischen Intent klassifizieren
        medical_intent = self.classify_medical_intent(voice_input)

        if medical_intent == 'appointment_booking':
            return self.book_appointment_by_voice(voice_input, patient_context)

        elif medical_intent == 'symptom_inquiry':
            return self.handle_symptom_discussion(voice_input)

        elif medical_intent == 'medication_info':
            return self.provide_medication_info(voice_input)

        else:
            return self.escalate_to_medical_staff(voice_input, patient_context)

    def book_appointment_by_voice(self, voice_input, patient_context):
        """
        Sprachbasierte Terminbuchung
        """
        appointment_details = self.extract_appointment_details(voice_input)

        available_slots = self.appointment_system.check_availability(
            doctor=appointment_details.get('preferred_doctor'),
            date_range=appointment_details.get('preferred_dates'),
            appointment_type=appointment_details.get('type')
        )

        if available_slots:
            response = f"""
            Ich habe passende Termine gefunden.
            Verfügbar sind: {', '.join([slot.strftime('%d.%m um %H:%M') for slot in available_slots[:3]])}
            Welcher Termin passt Ihnen am besten?
            """
        else:
            response = "Leider sind keine Termine in Ihrem gewünschten Zeitraum verfügbar. Soll ich alternative Zeiten vorschlagen?"

        return response

# E-Commerce Voice Shopping Assistant
class ECommerceVoiceAI:
    def __init__(self):
        self.product_catalog = ProductCatalog()
        self.recommendation_engine = RecommendationEngine()
        self.order_system = OrderSystem()

    def voice_shopping_experience(self, voice_input, customer_profile):
        """
        Sprachbasierte Shopping Experience
        """
        shopping_intent = self.classify_shopping_intent(voice_input)

        if shopping_intent == 'product_search':
            return self.search_products_by_voice(voice_input)

        elif shopping_intent == 'order_status':
            return self.check_order_status(voice_input, customer_profile)

        elif shopping_intent == 'recommendations':
            return self.voice_based_recommendations(customer_profile)

        elif shopping_intent == 'purchase':
            return self.complete_voice_purchase(voice_input, customer_profile)

    def search_products_by_voice(self, voice_input):
        """
        Sprachbasierte Produktsuche
        """
        search_criteria = self.extract_search_criteria(voice_input)

        products = self.product_catalog.search(
            query=search_criteria['product_name'],
            category=search_criteria.get('category'),
            price_range=search_criteria.get('price_range'),
            brand=search_criteria.get('brand')
        )

        if len(products) == 0:
            response = "Leider habe ich keine passenden Produkte gefunden. Können Sie Ihre Suche anders formulieren?"
        elif len(products) == 1:
            product = products[0]
            response = f"""
            Ich habe das perfekte Produkt gefunden: {product['name']}
            für {product['price']}€. {product['description']}
            Soll ich es in den Warenkorb legen?
            """
        else:
            top_products = products[:3]
            response = f"""
            Ich habe {len(products)} passende Produkte gefunden.
            Die beliebtesten sind: {', '.join([p['name'] for p in top_products])}
            Welches interessiert Sie am meisten?
            """

        return response

# Voice Biometrics für sichere Authentifizierung
class VoiceBiometrics:
    def __init__(self):
        self.voice_print_database = VoicePrintDatabase()
        self.fraud_detector = VoiceFraudDetector()

    def enroll_voice_print(self, user_id, voice_samples):
        """
        Registrierung eines Voice Prints
        """
        # Voice Features extrahieren
        voice_features = self.extract_voice_features(voice_samples)

        # Voice Print erstellen
        voice_print = self.create_voice_print(voice_features)

        # In Datenbank speichern (verschlüsselt)
        self.voice_print_database.store_encrypted(user_id, voice_print)

        return {'enrollment_successful': True, 'voice_print_id': voice_print.id}

    def authenticate_by_voice(self, voice_input, claimed_user_id):
        """
        Authentifizierung über Voice Biometrics
        """
        # Voice Features aus Input extrahieren
        input_features = self.extract_voice_features(voice_input)

        # Gespeicherten Voice Print laden
        stored_voice_print = self.voice_print_database.get(claimed_user_id)

        # Ähnlichkeit berechnen
        similarity_score = self.calculate_voice_similarity(
            input_features,
            stored_voice_print
        )

        # Fraud Detection
        fraud_indicators = self.fraud_detector.analyze(voice_input)

        authentication_result = {
            'authenticated': similarity_score > 0.85 and not fraud_indicators['is_fraud'],
            'confidence': similarity_score,
            'fraud_risk': fraud_indicators['risk_score'],
            'additional_verification_needed': similarity_score < 0.95
        }

        return authentication_result