The growing use of synthetic speech highlights the need to understand its differences from natural speech. Synthetic speech provides potential advantages in data augmentation, including privacy, security, and ethical data sourcing. However, the naturalness of synthetic speech is both a limitation and a risk, with deepfakes facilitating misinformation and fraud. These challenges underscore the need for improved methods to evaluate synthetic speech quality and deepfake detection. This research investigates whether latent representations from self-supervised models can identify and quantify differences between natural and synthetic speech regarding phoneme type, stress, manner, and roundedness. Our objective is to determine if models learn all phoneme categories equally or if certain groups present greater challenges, revealing limitations in synthetic speech. We pre-trained two wav2vec 2.0 models using matched natural and synthetic speech datasets. We mapped the learned codeword dictionaries to labeled test data with phoneme-level annotations and analysed the distribution and diversity of these latent representations across different phonemic categories. Our findings indicate a general lack of phonetic diversity in synthetic speech, with stress and manner showing the largest disparities. Vowels and diphthongs consistently exhibit reduced diversity. Identifying and quantifying differences in latent representations can be applied to enable enhanced synthetic speech generation, improve classification accuracy, and help to develop robust quality measurement metrics for synthetic speech.
Many recent Text-to-Speech (TTS) models employing zero-shot voice cloning techniques are capable of reproducing the emotional tone present in the reference speech. However, they frequently lack mechanisms for fine-grained emotion control due to the scarcity of labeled resources. The current work proposes a method for adjusting F5-TTS [1] in order to achieve fine-grained emotion control by fine-tuning under low-resource conditions, while aiming to keep the original model’s performance. To enforce the newly added condition, the classifier-free guidance (CFG) mechanism is modified by adding a supplementary term that amplifies the condition’s contribution, at the expense of increased inference time. We show that the proposed method is able to generate natural and intelligible speech with the same voice as the reference audio, but with changed emotion according to the label condition. Moreover, this method enables control over emotion intensity, despite the model not being explicitly trained for this task. Extensive automated evaluations and subjective tests with 20 human participants demonstrate that our method maintains intelligibility, naturalness and allows both emotion class and intensity control.Implementation code and demo samples are available at https://github.com/RaduBolbo/F5-TTS-Emotional-CFG
The rapid increase of computer-generated (CG) fake reviews on digital platforms poses a significant threat to consumer trust and market integrity. This research explores the effectiveness of established machine learning (ML) models in distinguishing CG reviews from original (OR), human-authored reviews, by using a public dataset of 40,432 labeled reviews. Comprehensive text preprocessing, including tokenization, stemming, and stop-word removal, is performed to standardize the data. Exploratory text analyses, namely word cloud visualization and sentiment scoring, reveal distinct lexical and emotional patterns between CG and OR reviews, with CG content displaying more constrained vocabulary and sentiment range. Multiple ML algorithms, including decision trees (DT), random forests (RF), and artificial neural networks (ANN), are systematically implemented and rigorously compared, with a primary focus on maximizing the recall metric for the CG class, in order to minimize the risk of undetected CG reviews. Results indicate that ANN models employing rectified linear unit (ReLU) activation achieve the highest recall, substantially reducing false negatives (FN) and enhancing the reliability of CG reviews detection. Feature analysis further highlights the importance of both lexical and sentiment-based attributes in differentiating review types. These findings underscore the practical effectiveness and computational efficiency of tailored ML approaches in safeguarding online review authenticity, supporting real-time deployment with low operational cost.
BLEU, ROUGE, and METEOR are widely used metrics for evaluating code summaries generated by large language models (LLMs), but their correlation with human judgment remains uncertain. In this study, we analyze whether a combination of these metrics can better approximate human evaluation scores. Using multilayer perceptrons (MLPs), Gaussian Process Regression (GPR) and Kolmogorov–Arnold Networks (KANs), we model the relationship between aggregated metric scores and human ratings. While individual metrics show weak alignment with human preferences particularly regarding semantic adequacy and contextual relevance, our results suggest that learned combinations offer modest improvements. Importantly, the interpretability of KANs provides insight into how these metrics interact. Rather than proposing a definitive new metric, this work offers a step toward understanding the limitations of current metrics and motivates the development of more human aligned evaluation methods for code summarization.
Echo cancellation is one of the most popular applications of adaptive filtering algorithms. In this framework, the algorithms have to be equipped with fast convergence/tracking features while should also be robust to different background perturbations. In terms of the convergence criteria, the decomposition-based recursive least-squares (RLS) algorithm represents a very appealing choice. It exploits an impulse response decomposition that relies on low-rank approximations and combines the estimates provided by two shorter adaptive filters using the nearest Kronecker product (NKP). In this paper, we develop a regularized version of the RLS-NKP algorithm with improved robustness features. The regularization components incorporate specific terms related to the background perturbations and model uncertainties, which are evaluated in a simple yet practical manner. Simulation results obtained in the context of network and acoustic echo cancellation support the performance gain.
