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.
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- 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.
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- 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.
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- 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.
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- 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:
- End-to-End Conversational Speech Models, such as:
- Conversational Speech Model Applications, such as:
- Conversational Speech Model Evaluation Frameworks, such as:
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- Conversational Speech Model Architectures, such as:
- 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.
References
2025
- (Iribe et al., 2025) ⇒ Brendan Iribe, Ankit Kumar, and the Sesame team. (2025). “Crossing the Uncanny Valley of Conversational Voice.”