End-to-End Speech Dialog Systems

• End-to-end speech-in speech-out dialogue systems are unified architectures that directly map spoken input to synthesized output, preserving paralinguistic cues like emotion and prosody.
• They overcome traditional cascaded limitations by eliminating explicit text representations, reducing error propagation and latency using integrated ASR, language understanding, and TTS modules.
• These systems leverage multi-modal training, token-based and layer-splitting methods, and real-time processing to enable robust, natural, and empathetic conversational interactions.

End-to-end speech-in speech-out dialogue systems are computational architectures that directly map incoming spoken utterances to outgoing synthesized speech, integrating all intermediate processing—such as speech recognition, language understanding, dialog management, and language generation—within a unified, often fully differentiable, model. Unlike traditional pipelines that sequentially link automatic speech recognition (ASR), natural language understanding/generation (NLU/NLG), and text-to-speech (TTS), these systems eliminate explicit intermediate text representations, enabling low-latency, expressive, robust conversational interaction and facilitating the modeling of paralinguistic and dialogic phenomena lost in cascaded text-centric frameworks (Ji et al., 15 Nov 2024).

1. Key Principles and Motivations

The end-to-end paradigm emerges from the need to reduce the limitations inherent in cascaded spoken dialog systems: error accumulation at module boundaries, increased latency from sequential processing, and loss of critical paralinguistic information such as prosody, emotion, and speaker-specific cues. In classical systems, ASR output serves as a bottleneck—semantic and stylistic elements essential for natural, empathetic conversation are often irretrievably abstracted away in textual form. By learning a direct mapping:

$\hat{S} = D\big( \mathrm{LLM}(E(S)) \big)$

where $S$ is the input speech, $E(\cdot)$ is a continuous or discrete speech encoder (e.g., HuBERT, Wav2Vec, Whisper), $\mathrm{LLM}(\cdot)$ is the LLM operating on speech tokens, and $D(\cdot)$ is the decoder (e.g., codec vocoder) rendering output speech, such systems preserve and manipulate a richer, more expressive signal (Ji et al., 15 Nov 2024).

The elimination of explicit intermediate text (as in MOSS-Speech (Zhao et al., 1 Oct 2025)) or relegating it to an intermediate reasoning step (as in chain-of-thought models (Arora et al., 31 May 2025, Arora et al., 2 Oct 2025)) enables preservation and generation of nuanced human speech behaviors—emotion, style, interactional cues—that are essential for naturalistic dialog.

2. Architectures and Training Paradigms

Modern architectures leverage large pre-trained LLMs, often starting from text-only GPT descendants and extending them for multi-modal operation (Zhang et al., 23 Oct 2024, Ji et al., 15 Nov 2024). Two dominant approaches are found:

• Token-based Modality Fusion: Here, continuous speech is mapped into sequences of discrete tokens (semantic or acoustic) which are then processed as if they are an extension of the word-level vocabulary of a text model. The LLM backbone is adapted post-hoc through multi-stage training that includes modality alignment (ASR/TTS), half-duplex, and full-duplex dialogue learning, as in the OmniFlatten model (Zhang et al., 23 Oct 2024).
• Modality-based Layer Splitting: In models such as MOSS-Speech (Zhao et al., 1 Oct 2025), the majority of layers (e.g., L₁–L₃₂ out of 36) are shared, fusing text and speech representations for joint understanding; then separate branches specialize for generating text or speech tokens, enabling direct speech-to-speech interaction without explicit text guidance.
• Chain-of-Thought Training: Sequential multi-stage reasoning with intermediate outputs—ASR transcript, text response, then TTS synthesis—are used as latent variables in both training and inference (Arora et al., 31 May 2025, Arora et al., 2 Oct 2025). This “CoT” approach increases interpretability, data efficiency, and semantic coherence, and allows for alignment with multi-stage pre-training tasks (ASR, TTS, LM).
• Empathy Modelling and Paralinguistic Reasoning: Architectures are increasingly including explicit modules or loss functions to extract and reason over emotion, speaker identity, age, and other social signals—e.g., OSUM-EChat’s dual chain-of-thought mechanism (Geng et al., 13 Aug 2025) and Style-Talker’s style vector integration (Li et al., 13 Aug 2024).
• Streaming and Duplex Interaction: End-to-end systems increasingly support real-time, full-duplex dialogues, alternately processing user input and generating overlapping system output in fixed-duration blocks (e.g., 2s), utilizing blockwise chain-of-thought with intermediate alignments (Arora et al., 2 Oct 2025). Self-attention and chunked token flattening enable the GPT-based backbone to model interruptions, backchannels, and natural turn-taking (Zhang et al., 23 Oct 2024).

Table: Representative End-to-End Speech-in Speech-out System Architectures

Model/Paper	Core Approach	Streaming/Duplex	Reasoning/CoT	Paralinguistic Modelling
MOSS-Speech (Zhao et al., 1 Oct 2025)	Layer-Splitting, No Text Intermediates	Yes	-	Strong (direct speech)
OmniFlatten (Zhang et al., 23 Oct 2024)	Token Flattening/GPT backbone	Yes	Optionally staged	Basic (via tokens)
SCoT (Arora et al., 2 Oct 2025)	Blockwise Chain-of-Thought streaming	Yes	Yes (blockwise CoT)	Limited
Style-Talker (Li et al., 13 Aug 2024)	Audio LLM + Style-TTS, Style Vectors	Yes	Implied in style context	Strong (explicit style)
OSUM-EChat (Geng et al., 13 Aug 2025)	Three-Stage (Understanding, Generation, Empathy); Dual Think	Yes	Yes (ling/paralinguistic)	Strong (dual reasoning)
Chain-of-Thought (Arora et al., 31 May 2025)	Explicit CoT (ASR→Text→TTS)	No (turn-based)	Yes	Limited

3. Speech Representation and Alignment

Central to the success of fully end-to-end dialogue systems is the representation of speech:

• Semantic and Acoustic Tokens: Discrete tokens derived from self-supervised speech models (HuBERT, Wav2Vec, Whisper) encode linguistic content, while acoustic tokens (e.g., from EnCodec, SpeechTokenizer, Mimi) encode prosody, timbre, and other non-linguistic cues (Ji et al., 15 Nov 2024).
• Cross-Modal Alignment: Tokenwise contrastive learning and cross-modal attention mechanisms have been employed to tightly align speech and text embeddings (token-by-token) (Sunder et al., 2022). This alignment enables better semantic transfer, data efficiency, and robustness in noisy or far-field conditions.
• Chain-of-Modality Reasoning: Some frameworks generate internal text responses as an auxiliary step to guide subsequent speech decoding, aligning the speech and language representations and stabilizing multi-stage training (Arora et al., 31 May 2025).

4. Enhanced Capabilities: Full-Duplex, Low Latency, and Human-Like Interaction

End-to-end speech-in speech-out systems now offer functionality previously unattainable:

• Full-Duplex/Streaming Dialogue: By integrating streaming, blockwise, or chunked processing (OmniFlatten’s flattening (Zhang et al., 23 Oct 2024), SCoT’s time-multiplexing (Arora et al., 2 Oct 2025)), these systems achieve low-latency, simultaneous bidsirectional interaction—supporting interruptions, backchannels, and overlaps, closely mirroring human dialog (Ji et al., 15 Nov 2024).
• Predictive Processing and Turn-Taking: Models can anticipate the end of user utterances (EOU) or next words using masked input training and cross-attention alignment (Zink et al., 30 Sep 2024), thereby shortening response times and aligning with the rapid turn-taking of natural conversation.
• Empathetic and Contextual Generation: Strategies such as dual chain-of-thought reasoning (Geng et al., 13 Aug 2025), style vector extraction (Li et al., 13 Aug 2024), and integration of dialog or prosodic history (Nishimura et al., 2022, Mitsui et al., 2022) enable nuanced generation, including empathy and context-aware prosody, critical for applications in assistive dialogue and emotionally intelligent agents.

5. Retrieval-Augmented and Knowledge-Grounded Dialogue

Incorporating external knowledge remains a technical challenge, especially due to the modality gap between spoken queries and textual resources:

• Direct Speech-Based Retrieval: Recent frameworks perform dense vector retrieval directly on spoken input by embedding both speech and text into a shared vector space, bypassing ASR entirely (Feng et al., 27 Apr 2025). This reduces retrieval latency dramatically (approximately one-fourth that of ASR-based pipelines), at only a marginal cost in retrieval accuracy.
• Retrieval-Augmented Generation: Retrieved knowledge (text) is injected as additional context or via chain-of-modality prompts, improving answer accuracy by up to 20–43% on targeted benchmarks relative to non-retrieval-augmented systems, though a performance gap to ASR-based approaches persists (Feng et al., 27 Apr 2025).

6. Data Efficiency, Evaluation, and Future Challenges

The development of robust, practical end-to-end speech-in speech-out systems is subject to several constraints and ongoing lines of research:

• Data Efficiency: Chain-of-thought strategies and multi-stage training aligned with pre-training tasks (ASR, TTS, LM) yield models that can be trained with as little as 300 hours of conversational data, compared to thousands typically required (Arora et al., 31 May 2025). Cross-modal and tokenwise alignment also reduce annotation burdens (Sunder et al., 2022, Cha et al., 2021).
• Benchmarking and Evaluation: Comprehensive benchmarks (e.g., EChat-eval (Geng et al., 13 Aug 2025), VoiceBench, SUPERB, MOS) and new metrics for full-duplex interaction, overlap prediction, emotional alignment, and streaming latency are being established to rigorously evaluate system performance (Ji et al., 15 Nov 2024).
• Limitations: Open challenges include data scarcity for real spoken dialogue, achieving unified compact representations that balance semantic accuracy and expressivity, modality alignment (especially for long, variable-length speech), catastrophic forgetting of text knowledge, and robustness in real-time noisy environments (Ji et al., 15 Nov 2024). Ensuring fairness, privacy, and secure deployment in sensitive scenarios remains a topic of future research.
• Open-Source and Community Resources: Several models, codebases, and datasets have been open-sourced, e.g., EChat-200K and GLM-Voice-RAG (Geng et al., 13 Aug 2025, Feng et al., 27 Apr 2025), facilitating broad adoption, reproducibility, and accelerated progress in the field.

7. Applications and Impact

End-to-end speech-in speech-out dialogue systems are increasingly relevant to:

• Conversational Agents and Voice Assistants: Providing low-latency, human-like interaction with expressive, emotionally aware feedback and robust handling of turn-taking and overlaps. Modern systems (e.g., GPT-4o, AudioGPT, Moshi, LLaMA-Omni) embody these properties (Ji et al., 15 Nov 2024).
• Healthcare and Assistive Technologies: Enhancing user experience through empathetic, context-sensitive, and accessible communication for diverse speakers, including those with atypical or impaired speech (Biadsy et al., 2019, Geng et al., 13 Aug 2025).
• Customer Service, Education, and Broadcasting: Supporting high-quality, natural, and adaptive spoken interaction in a wide variety of interactive, dynamic scenarios.
• Creative and Multimedia Content Creation: Enabling direct manipulation of spoken content for narration, dubbing, and real-time translation—leveraging the preservation of style, emotion, and interactional cues.

In conclusion, end-to-end speech-in speech-out dialogue systems represent a rapidly advancing area that integrates modern speech, language, and dialogue technologies in unified, flexible architectures. By emphasizing direct speech-to-speech modeling, deep contextual and paralinguistic understanding, and efficient, scalable training regimes, these systems are poised to underpin the next generation of interactive, intelligent spoken dialog applications (Zhao et al., 1 Oct 2025, Ji et al., 15 Nov 2024).