End-to-End Diarization+ASR Overview

• End-to-end diarization+ASR is a unified framework that integrates automatic speech recognition and speaker diarization to generate speaker-attributed transcripts with precise time alignment.
• Techniques such as unified encoder-decoder architectures, auxiliary diarization heads, and token interleaving ensure improved temporal alignment and reduced error propagation.
• Empirical evaluations demonstrate competitive performance with lower error rates in multi-speaker scenarios, though challenges persist in high-overlap and noisy conditions.

End-to-end diarization+ASR refers to a family of neural architectures and frameworks that jointly perform automatic speech recognition (ASR) and speaker diarization within a single, unified system. The principal aim is to address "who spoke what (and when)" without relying on modular, cascaded pipelines, thus reducing error propagation and enabling more efficient and robust transcription and speaker attribution. These models take as input audio—often of long-form conversations with multiple, potentially overlapping speakers—and yield speaker-attributed transcripts, frequently with fine-grained word or utterance-level time alignment.

1. Architectural Principles and Core Design Patterns

End-to-end diarization+ASR systems fall into several broad architectural paradigms:

• Unified Encoder-Decoder Backbones: Many systems extend attention-based encoder-decoder architectures (e.g., Whisper, Conformer, Transformer) to interleave ASR and diarization functionalities. The encoder processes the input features (typically log-Mel filterbanks), while the decoder produces serialized output streams containing transcripts, speaker labels, and often timestamp tokens (Xu et al., 25 Jan 2026, Kocour et al., 4 Oct 2025, Cornell et al., 2023, Park et al., 2024, Huo et al., 11 Jan 2026).
• Auxiliary Diarization Heads: Frame-level diarization heads are commonly attached to the final encoder layers, predicting speaker activity or role classes per frame. This can be integrated as an auxiliary supervision signal with a cross-entropy loss (Xu et al., 25 Jan 2026, Khare et al., 2022, Taherian et al., 2023).
• Multi-Branch or Dual-Stream Encoders: Some frameworks, such as TagSpeech and certain LLM-based models, split the semantic (ASR) and speaker-tracing encoders, each projecting to the shared decoder or LLM input space. Capabilities such as temporal-anchoring or speaker-turn encoding are achieved by the interleaving of numeric tokens or tags (Huo et al., 11 Jan 2026).
• Neural Diarization-Conditioned Encoders: Architectures like Diarization-Conditioned Whisper (DiCoW) adaptively modulate encoder activations using input diarization masks or speaker role alignments to provide a more explicit speaker-aware context for ASR decoding (Kocour et al., 4 Oct 2025).
• Streaming and Sliding-Window Processing: To handle arbitrarily long inputs and maintain tractable computation, windows or audio chunks are processed independently, and speaker embeddings are clustered globally post hoc to resolve consistent identities (Cornell et al., 2023, Shi et al., 20 Nov 2025).

2. Serialized Output Training and Joint Decoding

A signature feature is the serialization of diarization, ASR, and (if needed) timestamp tokens into a unified autoregressive or non-autoregressive output sequence.

• Token Interleaving: Systems emit sequences such as: $$\langle|\text{t\_start}|\rangle\,\langle\text{spk}\rangle\,\text{word}_1\,\cdots\,\text{word}_K\,\langle|\text{t\_end}|\rangle$$, where special tokens mark utterance/timestamp boundaries and speaker changes (Xu et al., 25 Jan 2026, Kocour et al., 4 Oct 2025, Cornell et al., 2023).
• Segment, Word, or Subword-Level Granularity: Label assignment may occur at the utterance, word, or subword level, with trade-offs in alignment precision and model complexity. For instance, auxiliary networks in WEEND predict a speaker label per wordpiece (Huang et al., 2023), while SOT systems produce blocks per utterance.
• Forced Decoding and State Machines: Some models, notably those extending Whisper, use finite-state constraints during decoding to ensure the output sequence adheres to a valid serialization grammar (e.g., ensuring every speech segment starts and ends with a timestamp and speaker tag). This eliminates structurally invalid transcripts (Xu et al., 25 Jan 2026).

3. Training Objectives and Losses

End-to-end diarization+ASR training typically employs composite losses spanning recognition and diarization tasks:

• ASR Loss: The cross-entropy between predicted and ground-truth transcript tokens (often using teacher forcing).
• Diarization Loss: Frame-level, speaker-wise cross-entropy (or permutation-invariant loss in multi-speaker diarization) for predicting speaker activity or role assignment (Park et al., 2024, Khare et al., 2022).
• Total Multi-Task Loss: Weighted sum of ASR and diarization losses. Regularization and auxiliary objectives (such as CTC, intermediate losses, or speaker classification/embedding alignment) are frequently used (Xu et al., 25 Jan 2026, Li et al., 2023, Park et al., 2024).
• Label Synchronization Strategies: Models like TagSpeech use a single cross-entropy over an interleaved token set containing words, speaker IDs, and time anchors (Huo et al., 11 Jan 2026).

4. Techniques for Improved Structural and Temporal Alignment

Several mechanisms improve transcript structural validity and temporal alignment:

• Diarization-Guided Silence Suppression: During decoding, predicted silence regions from the diarization head mask out candidate timestamp tokens falling inside silence, which sharpens utterance boundary placement (Xu et al., 25 Jan 2026).
• State-Machine/Grammar-Based Output Constraints: Decoding enforces language-model constraints by masking out illegal tokens for the current state to guarantee well-formed outputs, virtually eliminating missing token and infinite loop errors in SOT (Xu et al., 25 Jan 2026).
• Fine-Grained Temporal Grounding: The use of explicit temporal anchor tokens (numeric or symbolic) introduced into both semantic and speaker streams enables fine time-alignment between content and diarization (Huo et al., 11 Jan 2026).

5. Empirical Performance and Benchmarks

Empirical evaluations consistently demonstrate substantial improvements in multi-speaker recognition and diarization accuracy:

System	Dataset	mtWER/cpWER Change vs. Baseline	DER (Diarization Error Rate)	Reference
Whisper-based E2E SOT	Playlogue	–8.0 pp (17.6% rel)	40–43% (vs. 35–36% cascaded diar)	(Xu et al., 25 Jan 2026)
	ADOS-Mod3	–5.3 pp (19.6% rel)	19–22% (matches best baseline)
SA-DiCoW	AMI-SDM	18.1% cpWER (vs. 21.1% baseline)	Not reported (oracle diarization used)	(Kocour et al., 4 Oct 2025)
Sortformer (hybrid)	DIHARD3	—	14.8% (matches or beats SOTA EEND-EDA)	(Park et al., 2024)
TagSpeech	AMI-SDM	DER 24.84% (–28% rel vs best E2E)	SCA=70%	(Huo et al., 11 Jan 2026)
SA-Paraformer	AliMeeting	SD-CER 34.8% (–6.1% rel vs. casc.)	RTF=0.032 (10× faster than AR)	(Li et al., 2023)

These results indicate end-to-end models deliver competitive or superior speaker-attributed WER/cpWER compared to cascaded pipelines, with simultaneous diarization performance approaching traditional diarization systems. On clean data, these systems often close the performance gap entirely, with lingering challenges primarily in noisy, high-overlap, or 3+ speaker conditions.

6. Broader Methodological Innovations and Extensions

Prominent research directions and innovations include:

• Non-Autoregressive Decoding: Paraformer-based architectures enable fully parallel token prediction, drastically reducing inference latency without loss in accuracy (Li et al., 2023).
• Permutation-Resolution Losses: Sortformer introduces “Sort Loss”, replacing computationally expensive permutation-invariant losses with sorting-based objectives, resolving speaker-channel ambiguity efficiently (Park et al., 2024).
• Role Diarization and Specialized Predictors: Models can be tuned for role diarization (e.g., child-adult, doctor-patient), sometimes requiring separate predictors for word and role tokens, with forced-alignment-based training for label synchronization (Ghosh et al., 14 Jul 2025, Xu et al., 25 Jan 2026).
• LLM-Integrated and Parameter-Efficient Paradigms: Architectures leveraging multimodal LLMs (e.g., Qwen-2.5, Phi-4, SenseVoice) use lightweight adapters or projectors for efficient transfer and modular finetuning (Yin et al., 8 Aug 2025, Shi et al., 20 Nov 2025, Huo et al., 11 Jan 2026).
• Streaming and Long-Form Inference: Frameworks like JEDIS-LLM achieve zero-shot streamable inference on recordings far exceeding training chunk length by introducing a Speaker Prompt Cache and synchronized chunk-wise outputs (Shi et al., 20 Nov 2025).

7. Challenges, Limitations, and Future Work

Despite rapid progress, the following challenges persist:

• Overlap and Scalability: Recognition and diarization accuracy still degrades in high-overlap, highly multi-speaker scenarios. Extension to longer-form and online/streaming contexts involves managing context state and speaker consistency across windows (Cornell et al., 2023, Park et al., 2024).
• Structural Guarantees and Robustness: Ensuring structurally valid outputs (correct marking of all segments, tokens) requires forced decoding machinery, which may introduce decoding complexity (Xu et al., 25 Jan 2026).
• Internal Alignment: Precise alignment of words to speakers and timestamps can be limited by inherent model stride or by noisy diarization heads in challenging audio.
• Role and Attribute Generalization: Expanding diarization to arbitrary roles (beyond generic speaker IDs) or attributes (e.g., gender, emotion) is an ongoing research area (Ghosh et al., 14 Jul 2025, Xu et al., 25 Jan 2026).
• Evaluation and Generalization: Performance remains variable with domain mismatch, unseen languages, or when scaling to large numbers of speakers (Yin et al., 8 Aug 2025, Park et al., 2024). Multi-stage training and domain adaptation are active research directions.

Future work targets unified loss architectures, improved handling of overlaps, deeper integration with multimodal LLMs, and streaming/real-time deployment strategies, including joint modeling of diarization, ASR, and speaker attributes in a fully end-to-end fashion.