The current study investigated the Lombard effect on acoustic features of discourse and prosodic words produced by native Chinese speakers. The results showed that on the discourse level, the overall intensity was significantly increased, vowel duration was significantly lengthened, while the fundamental frequency (F0) was slightly increased for the speech produced in noise conditions compared to that produced in quiet conditions. Further analysis illustrated the Lombard effect on acoustic features of prosodic words. The results showed that the high-pitched point and low-pitched point were significantly increased, the vowel duration was significantly lengthened, while the pitch range was slightly wider for the speech produced in noise conditions compared to that produced in quiet conditions. These findings shed light on the Lombard effect on the acoustic characteristics of prosodic production among native Chinese speakers in noise conditions.
This study presents a quantitative evaluation of translation quality and computational efficiency between semantic and phonetic translation strategies for Chinese scientific terms, using Large Language Models (LLMs). Focusing on two representative domains—the Chinese periodic table and heterocyclic compound nomenclature—we propose a back-translation framework (LLM-BT) specifically designed for evaluating scientific terminology translation, conducting Chinese→ English→ Chinese cycles with Grok, DeepSeek, and GPT-4 models. BLEU scores are used to assess translation accuracy. Results show that semantic translation of element names achieves near-perfect performance (BLEU ≈1.00), leveraging the compositional properties of Chinese characters. In contrast, phonetic translation of heterocyclic compounds yields lower BLEU scores (0.55-0.65), reflecting the inherent challenges of transliteration. Multilingual comparisons using Google Translate (Chinese, Japanese, Korean, English) further validate the superiority of semantic translation strategies (BLEU 0.94-0.99) over phonetic approaches (0.16-0.36). Also, byte-level analysis reveals that Chinese semantic and pictophonetic names are more compact (3 bytes per element) compared to Japanese (13.73 bytes) and Korean (8.97 bytes) transliterations. These findings highlight the dual advantages of semantic translation for both accuracy and computational efficiency. We advocate prioritizing semantic strategies in scientific terminology standardization and cross-linguistic knowledge transfer.
Currently, speech synthesis has found increasingly widespread applications across various fields. Although the quality of synthesized speech continues to improve, challenges remain in emotional speech synthesis, particularly in achieving naturalness and authentic emotional expression. Additionally, traditional emotional speech synthesis models often suffer from low generation efficiency due to their complex processing pipelines. To address these two issues, a LightGrad-based emotional speech synthesis model is proposed in this paper, featuring a constructed feature fusion module, along with a gated style token layer and a prosody predictor incorporated into the model. The gated style token layer helps generate emotional style embedding vectors to capture more subtle and complex emotional details. The prosody predictor provides prosodic information for audio generation to improve the rhythm, pausing patterns, and natural transitions of intonation in speech. In addition, dilated causal convolutions are used to replace depthwise separable convolutions, enhancing the model's ability to model sequential data, while optimal transport conditional flow matching (OT-CFM) is employed to improve the diffusion decoder, effectively increasing the synthesis efficiency of the model. Experimental results demonstrate that the proposed model can generate emotionally expressive speech while maintaining high synthesis quality. Moreover, training on both English and Chinese corpora verifies its cross-lingual capability.
Sarcasm's nature of inverting the implied sentiment makes it difficult to perceive. While sarcasm detection has seen significant development in recent years, most research has focused on the English language, limiting its applicability to sentiment-related tasks in low-resource languages like Filipino. In this work, we address this gap by leveraging synthetic data and large language models (LLMs) to create a textual Filipino sarcasm detection dataset and assess LLM performance on the task. We utilize LLMs due to their capability to generate data quickly with controlled distribution at a much lower cost and the opportunity to test their understanding of the subtle forms of sarcasm, as well as the Filipino language's linguistic and cultural nuances. Our dataset of 1008 synthetic and 100 real-world tweets enabled Gemini 2.0 Flash to achieve 96% accuracy with few-shot prompting on synthetic data. However, performance dropped to 74% on real-world data with zero-shot prompting. This discrepancy highlights the limitation of synthetic data in capturing the authenticity of real-world language and points to the need for fine-tuning LLMs to better adapt to the nuances of Filipino sarcasm detection.
