First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes an algorithm to learn a distance metric for time series alignment. The proposed method falls into the structured output prediction framework, and is solved by a combination of convex optimization and dynamic programming. The method is evaluated on synthetic and realistic audio alignment tasks, and demonstrates significant improvement over baseline methods. Overall, this paper presents an interesting method for a real problem faced by practitioners dealing with time-series alignment tasks. The paper is generally well written and easy to follow, although a few points could be stated more clearly.

Personal use of this material is permitted. This work has been accepted for publication in the proceedings of the 2025 Advances in Science and Engineering Technology International Conferences (ASET). Zaheeruddin Ahmed Department of Computer Science & Engineering Manipal Academy of Higher Education Dubai, UAE zaheeruddin@manipaldubai.com Abstract -- Audio - based equipment condition monitoring suffers from a lack of standardized methodologies for algorithm selection, hindering reproducible research. Leveraging a rich 127 - feature set across time, frequency, and time - frequency domains, our methodology is validated on both synthetic and real - world datasets. Results demonstrate that an ensemble method achieves superior performance (94.2% accuracy, 0.942 F1 - score), with statistical testing confirming its significant outperformance of individual algorithms by 8 - 15%.

Sarcasm, a common feature of human communication, poses challenges in interpersonal interactions and human-machine interactions. Linguistic research has highlighted the importance of prosodic cues, such as variations in pitch, speaking rate, and intonation, in conveying sarcastic intent. Although previous work has focused on text-based sarcasm detection, the role of speech data in recognizing sarcasm has been underexplored. Recent advancements in speech technology emphasize the growing importance of leveraging speech data for automatic sarcasm recognition, which can enhance social interactions for individuals with neurodegenerative conditions and improve machine understanding of complex human language use, leading to more nuanced interactions. This systematic review is the first to focus on speech-based sarcasm recognition, charting the evolution from unimodal to multimodal approaches. It covers datasets, feature extraction, and classification methods, and aims to bridge gaps across diverse research domains. The findings include limitations in datasets for sarcasm recognition in speech, the evolution of feature extraction techniques from traditional acoustic features to deep learning-based representations, and the progression of classification methods from unimodal approaches to multimodal fusion techniques. In so doing, we identify the need for greater emphasis on cross-cultural and multilingual sarcasm recognition, as well as the importance of addressing sarcasm as a multimodal phenomenon, rather than a text-based challenge.

Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate phonetic representation.

The dynamic characteristics of speech signal provides temporal information and play an important role in enhancing Automatic Speech Recognition (ASR). In this work, we characterized the acoustic transitions in a ratio plane of Spectral Subband Centroid Frequencies (SSCFs) using polar parameters to capture the dynamic characteristics of the speech and minimize spectral variation. These dynamic parameters were combined with Mel-Frequency Cepstral Coefficients (MFCCs) in Vietnamese ASR to capture more detailed spectral information. The SSCF0 was used as a pseudo-feature for the fundamental frequency (F0) to describe the tonal information robustly. The findings showed that the proposed parameters significantly reduce word error rates and exhibit greater gender independence than the baseline MFCCs.

The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral feature representation, capturing the temporal patterns inherent in network traffic more effectively than traditional fixed MFCCs. We demonstrate that transforming raw signals into MFCCs maps the data into a higher-dimensional space, enhancing class separability and enabling more effective multiclass classification. Our approach combines the strengths of MFCCs with the robust feature extraction capabilities of ResNet-18, offering a powerful framework for anomaly detection. The proposed model is evaluated on three widely used IoT intrusion detection datasets: CICIoT2023, NSL-KDD, and IoTID20. The experimental results highlight the potential of integrating adaptive signal processing techniques with deep learning architectures to achieve robust and scalable anomaly detection in heterogeneous IoT network landscapes.

Air traffic control (ATC) demands multi-tasking under time pressure with high consequences of an error. This can induce stress. Detecting stress is a key point in maintaining the high safety standards of ATC. However, processing ATC voice data entails privacy restrictions, e.g. the General Data Protection Regulation (GDPR) law. Anonymizing the ATC voice data is one way to comply with these restrictions. In this paper, different architectures for stress detection for anonymized ATCO speech are evaluated. Our best networks reach a stress detection accuracy of 93.6% on an anonymized version of the Speech Under Simulated and Actual Stress (SUSAS) dataset and an accuracy of 80.1% on our anonymized ATC simulation dataset. This shows that privacy does not have to be an impediment in building well-performing deep-learning-based models.

Human emotional expression emerges through coordinated vocal, facial, and gestural signals. While speech face alignment is well established, the broader dynamics linking emotionally expressive speech to regional facial and hand motion remains critical for gaining a deeper insight into how emotional and behavior cues are communicated in real interactions. Further modulating the coordination is the structure of conversational exchange like sequential turn taking, which creates stable temporal windows for multimodal synchrony, and simultaneous speech, often indicative of high arousal moments, disrupts this alignment and impacts emotional clarity. Understanding these dynamics enhances realtime emotion detection by improving the accuracy of timing and synchrony across modalities in both human interactions and AI systems. This study examines multimodal emotion coupling using region specific motion capture from dyadic interactions in the IEMOCAP corpus. Speech features included low level prosody, MFCCs, and model derived arousal, valence, and categorical emotions (Happy, Sad, Angry, Neutral), aligned with 3D facial and hand marker displacements. Expressive activeness was quantified through framewise displacement magnitudes, and speech to gesture prediction mapped speech features to facial and hand movements. Nonoverlapping speech consistently elicited greater activeness particularly in the lower face and mouth. Sadness showed increased expressivity during nonoverlap, while anger suppressed gestures during overlaps. Predictive mapping revealed highest accuracy for prosody and MFCCs in articulatory regions while arousal and valence had lower and more context sensitive correlations. Notably, hand speech synchrony was enhanced under low arousal and overlapping speech, but not for valence.

The range of potential applications of acoustic analysis is wide. Classification of sounds, in particular, is a typical machine learning task that received a lot of attention in recent years. The most common approaches to sound classification are sub-symbolic, typically based on neural networks, and result in black-box models with high performances but very low transparency. In this work, we consider several audio tasks, namely, age and gender recognition, emotion classification, and respiratory disease diagnosis, and we approach them with a symbolic technique, that is, (modal) decision tree learning. We prove that such tasks can be solved using the same symbolic pipeline, that allows to extract simple rules with very high accuracy and low complexity. In principle, all such tasks could be associated to an autonomous conversation system, which could be useful in different contexts, such as an automatic reservation agent for an hospital or a clinic.

The development of electronic stethoscopes and wearable recording sensors opened the door to the automated analysis of bowel sound (BS) signals. This enables a data-driven analysis of bowel sound patterns, their interrelations, and their correlation to different pathologies. This work leverages a BS dataset collected from 16 healthy subjects that was annotated according to four established BS patterns. This dataset is used to evaluate the performance of machine learning models to detect and/or classify BS patterns. The selection of considered models covers models using tabular features, convolutional neural networks based on spectrograms and models pre-trained on large audio datasets. The results highlight the clear superiority of pre-trained models, particularly in detecting classes with few samples, achieving an AUC of 0.89 in distinguishing BS from non-BS using a HuBERT model and an AUC of 0.89 in differentiating bowel sound patterns using a Wav2Vec 2.0 model. These results pave the way for an improved understanding of bowel sounds in general and future machine-learning-driven diagnostic applications for gastrointestinal examinations