Singapore

AAAI 2026

Granular Control of Nonverbal Expressions for Achieving Natural Emotional Text-to-Speech

expression control

fine-grained approach

non-verbal vocalizations

emotional speech

text-to-speech synthesis

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio Embedding Sequences

Can You Hear Naples? Building and Benchmarking a Neapolitan Speech Corpus

AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval

Generalizable speech deepfake detection via meta-learned LoRA

Toward High-Quality Cross-lingual Text-to-Speech Synthesis In Low-Resource Scenarios

Semi-supervised Acoustic Scene Classification under Spatial-Temporal Variability with a CRNN-based Model

Online Independent Low-Rank Matrix Analysis as a Lightweight and Trainable Model for Real-Time Multichannel Music Source Separation

Deep learning for time-series anomaly detection (TSAD) 
has gained significant attention over the past decade. 
Despite the reported improvements in several papers, 
the practical application of these models remains limited. 
Recent studies have cast doubt on these models, 
attributing their results to flawed evaluation techniques. 
However, the impact of initialization has largely been 
overlooked. This paper provides a critical analysis of the 
initialization effects on TSAD model performance. 
Our extensive experiments reveal that TSAD models are 
highly sensitive to hyperparameters such as window size, 
seed number, and normalization. This sensitivity often leads 
to significant variability in performance, which can be 
exploited to artificially inflate the reported efficacy of these 
models. We demonstrate that even minor changes in 
initialization parameters can result in performance variations 
that overshadow the claimed improvements from novel 
model architectures. Our findings highlight the need for 
rigorous evaluation protocols and transparent reporting 
of preprocessing steps to ensure the reliability and fairness 
of anomaly detection methods. This paper calls for a more 
cautious interpretation of TSAD advancements and 
encourages the development of more robust and 
transparent evaluation practices to advance the field 
and its practical applications.

Unveiling the Flaws:

The progress of Anomaly Detection (AD) in safety-critical 
domains, such as transportation, is severely constrained 
by the lack of large-scale, real-world benchmarks. 
To address this, we introduce EngineAD, a novel, 
multivariate dataset comprising high-resolution sensor 
telemetry collected from a fleet of 25 commercial vehicles 
over a six-month period. Unlike synthetic datasets, 
EngineAD features authentic operational data labeled 
with expert annotations, distinguishing normal states from 
subtle indicators of incipient engine faults. We preprocess 
the data into 300-timestep segments of 8 principal 
components and establish an initial benchmark using 
nine diverse one-class anomaly detection models. 
Our experiments reveal significant performance variability 
across the vehicle fleet, underscoring the challenge of 
cross-vehicle generalization. Furthermore, our findings 
corroborate recent literature, showing that simple 
classical methods (e.g., K-Means and One-Class SVM) 
are often highly competitive with, or superior to, deep 
learning approaches in this segment-based evaluation. 
By publicly releasing EngineAD, we aim to provide a 
realistic, challenging resource for developing robust 
and field-deployable anomaly detection and anomaly 
prediction solutions for the automotive industry.

EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset

As notoriously opaque deep neural networks (DNNs) become commonplace in powerful Artificial Intelligence (AI) systems, Interpretable Deep Learning (IDL) has emerged as a promising direction for designing interpretable-by-construction neural architectures. At their core, IDL models learn a latent space where some of their representations are aligned with high-level units of information, or concepts, that domain experts are familiar with (e.g., “stripped texture”,  “round object”, etc.). By introducing inductive biases that encourage predictions to be made based on these interpretable representations, IDL models enable the construction of expressive yet highly transparent architectures that can be vetted, analysed, and intervened on.

This tutorial aims to capitalise on the surge of interest in IDL by exposing AI researchers and engineers to the core foundations necessary to understand the general principles behind existing IDL models. By doing so, we aim to equip attendees with the knowledge necessary to comprehend the current state of this extensive body of literature, enabling them to build upon it for their research. Specifically, this tutorial will provide an overview of (1) core interpretability principles, (2) seminal works in the field, and (3) recent directions in IDL. Our tutorial will include hands-on demonstrations throughout our session and will conclude with a discussion of the key open questions in the field.

Our material will assume a basic knowledge of ML (e.g., foundations of supervised learning, experimental design, basic probabilistic modelling, etc.), with particular emphasis on a solid Deep Learning foundation (e.g., tensor calculus, neural networks, backpropagation, etc.). Concepts that may require mathematical tools/expertise beyond those one would expect to be shared among the AAAI community will be (re)introduced in our tutorial.

For further details and material, please refer to our tutorial’s site found at https://interpretabledeeplearning.github.io/.

Next from AAAI 2026

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio Embedding Sequences

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