Conversational Speech Model

A Conversational Speech Model is a speech model that is a conversational model designed to generate natural, contextually appropriate speech for interactive dialogue systems.

• Context:
  • It can typically generate Conversational Speech Output with conversational speech quality that adapts to the ongoing dialogue context.
  • It can typically maintain Conversational Prosody through conversational speech generation techniques that account for emotional and contextual factors.
  • It can typically process Conversational Context through conversational history tracking to inform appropriate speech responses.
  • It can typically model Conversational Turn-Taking through conversational timing mechanisms.
  • It can typically represent Conversational Emotional State through conversational tone modulation.
  • ...
  • It can often integrate Multimodal Input through conversational multimodal processing to enhance contextual understanding.
  • It can often support Real-Time Response Generation through low-latency architectures.
  • It can often implement Speaker Consistency through speaker identity preservation techniques.
  • It can often adapt Conversational Style through conversational adaptation mechanisms based on the dialogue partner.
  • ...
  • It can range from being a Simple Conversational Speech Model to being a Complex Conversational Speech Model, depending on its conversational capability scope.
  • It can range from being a Text-Driven Conversational Speech Model to being a Multimodal Conversational Speech Model, depending on its input modality support.
  • It can range from being a Single-Speaker Conversational Speech Model to being a Multi-Speaker Conversational Speech Model, depending on its speaker modeling capacity.
  • ...
  • It can incorporate Semantic Token processing for conversational semantic representation.
  • It can utilize Acoustic Token processing for high-fidelity speech reconstruction.
  • It can employ Residual Vector Quantization for detailed acoustic modeling.
  • It can leverage Transformer Architecture for contextual speech generation.
  • It can implement End-to-End Speech Synthesis for unified conversational modeling.
  • ...
• Examples:
  • Conversational Speech Model Architectures, such as:
    • End-to-End Conversational Speech Models, such as:
      • Sesame Conversational Speech Model (2025), utilizing a multimodal backbone transformer with audio decoder for contextually appropriate speech.
      • Two-Stage Conversational Speech Models for sequential semantic and acoustic processing.
    • Multimodal Conversational Speech Models, such as:
      • Text-Audio Conversational Speech Models for interleaved text and audio processing.
      • Context-Aware Conversational Speech Models for dialogue history integration.
  • Conversational Speech Model Applications, such as:
    • Voice Assistant Conversational Speech Models, such as:
      • Personal Assistant Conversational Speech Models for daily task interaction.
      • Customer Service Conversational Speech Models for customer inquiry handling.
    • Interactive Entertainment Conversational Speech Models, such as:
      • Game Character Conversational Speech Models for immersive gaming experience.
      • Story Narration Conversational Speech Models for interactive storytelling.
  • Conversational Speech Model Evaluation Frameworks, such as:
    • Objective Conversational Speech Model Evaluations, such as:
      • Word Error Rate Evaluations for conversational speech accuracy assessment.
      • Homograph Disambiguation Tests for conversational speech understanding evaluation.
    • Subjective Conversational Speech Model Evaluations, such as:
      • Comparative Mean Opinion Scores for human preference assessment.
      • Contextual Appropriateness Evaluations for conversational continuity assessment.
  • ...
• Counter-Examples:
  • Traditional Text-to-Speech Systems, which lack conversational context sensitivity and dialogue adaptation capability.
  • Pre-recorded Voice Response Systems, which lack speech generation flexibility and real-time response capability.
  • Non-Conversational Speech Models, which focus on monologue generation rather than interactive dialogue.
  • Speech Recognition Models, which process speech input rather than generate conversational speech output.
• See: Speech Synthesis System, Conversational AI, Natural Language Generation Model, Voice Presence System, Multimodal Speech Generation Framework.
• References:
  • Sesame Research Team. (2025). "Conversational Speech Generation." Retrieved from Sesame AI technical post.

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References

2025

• (Iribe et al., 2025) ⇒ Brendan Iribe, Ankit Kumar, and the Sesame team. (2025). “Crossing the Uncanny Valley of Conversational Voice.”

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