Conference Information

SLT 2026: IEEE Spoken Language Technology

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Submission Date:
2026-09-11 Closed
Notification Date:
2026-09-16
Conference Date:
2026-12-13
Location:
Palermo, Sicily, Italy
CCF: C   Viewed: 3144   Tracked: 2   Attend: 1
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Conference Partner Index (CP-I)

52.2 / 100
Ranked #1,257 of 5,764 conferences · Top 22%

#22 of 85 in Natural Language Processing & Speech

Academic recognition (35%)
66
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%) No data - scored at the neutral baseline of 50 —
Community attention (10%)
39
Public record completeness (15%)
35

Inputs used: Listed as CCF C · Researchers following it here: 2 · Researchers who opened this page in the past 24 months: 41

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 60% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-03

Call For Papers

SLT 2026 (IEEE Spoken Language Technology) is a CCF C conference held in Palermo, Sicily, Italy on 2026-12-13. The paper submission deadline is 2026-09-11 (extended). Acceptance notifications are sent on 2026-09-16.

The IEEE Spoken Language Technology (SLT) Workshop, held biennially, brings together leading researchers from academia and industry in a focused and collaborative environment. As a pivotal forum for discussing emerging developments and shared challenges in spoken language technology, SLT fosters meaningful dialogue, the exchange of cutting-edge research, and the advancement of the field. Transforming how we connect and innovate, spoken language technology profoundly impacts human progress by driving the future of interaction, accelerating innovation, ensuring greater accessibility, and enabling seamless human-machine interaction. Its impact extends across numerous domains, including education and healthcare, where it is transforming how technology is developed and deployed. We look forward to welcoming you to SLT 2026 for an enriching experience as we explore the latest innovations and continue pushing the frontiers of this dynamic field. Topics We are calling for submissions in all areas of spoken language processing, particularly focusing on the following topics: Automatic speech recognition Conversational/multi-speaker ASR Far-field speech processing Speech separation and enhancement Speaker and language recognition Speaker diarization Spoken language understanding Spoken dialog systems Low resource/multilingual speech processing Spoken language processing Spoken document retrieval Speech-to-speech translation Text-to-speech systems Speech summarization New applications of automatic speech recognition Audio-visual/multimodal speech processing Emotion recognition from speech Large language models for spoken language processing Speech foundation models Self-supervised and representation learning for speech Child speech/language processing Special Theme: Spoken Language Technology for Social Wellbeing in the Digital Privacy Era Since the theme of this year is Spoken Language Technology for Social Wellbeing in the Digital Privacy Era, the following key topics are also proposed: Voice Technology in Healthcare Speech Processing for Assistive Healthcare Technologies Assistive Hearing and Speaking Technologies Voice-Activated Virtual Healthcare Assistants Speech Technologies for Telehealth and Remote Care Privacy-preserving speech recognition Privacy in Voice Biometrics and Speaker Identification Robustness of speech technologies against malicious activities, adversarial attacks, and threat models Fairness, bias, and accountability in speech systems Spoofing and deepfake detection, attribution, and characterization Forensic and investigative applications Voice Anonymization Techniques Integrity verification, watermarking, and traceability for voice technologies
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