Session variability caused by channel changes and acoustic conditions often degrades the performance of i-vector-based speaker identification. This study applies data augmentation to mitigate such effects and improve system robustness. We compile a dataset from five Indonesian speech corpora and create six recording conditions: clean, additive noise, two levels of reverberation, gain adjustment, and speed perturbation. Each signal is preprocessed into 39 -dimensional MFCCs, a 128 component GMM-UBM is trained, 200 -dimensional i-vectors are extracted, and similarity is scored using Linear Discriminant Analysis (LDA) with cosine distance and Probabilistic LDA (PLDA). A factorial design yields eight configurations that combine training augmentation on or off with four overlap levels between training and enrollment data. Evaluation across four enrollment scenarios uses accuracy and Equal Error Rate (EER). Results show that augmentation does not always increase accuracy for LDA+cosine distance, with the best accuracy of 98.5 % obtained without augmentation. In contrast, PLDA benefits consistently from augmentation, reaching accuracy up to 94.7 % and reducing EER to 1.74 %, especially under full overlap when augmentation is applied symmetrically to both training and enrollment. These findings indicate that symmetric augmentation paired with PLDA significantly improves the robustness of i -vector speaker identification against session variability.
This project develops a chatbot module for an Intelligent Tutoring System (ITS) in English language learning, powered by a large language model (LLM). The chatbot provides personalized practice, real-time grammar correction, vocabulary support, and question answering, adapting to each student's proficiency. Integrated with student and tutor modules, it tracks progress and tailors feedback dynamically. Leveraging LLM capabilities, the system understands natural language inputs and generates contextually relevant responses, enhancing engagement and learning outcomes. Evaluation focuses on accuracy, informativeness, and user satisfaction, offering a scalable AI-driven solution to improve English proficiency efficiently.
Tones are pitch variations on syllables that distinguish meaning in Chinese and reflect its phonetic characteristics. Mastery of tones directly impacts learners' Chinese proficiency. This study employs experimental phonetics to analyze the production of neutral tone, T2 and T3 by Chinese learners from five Central Asian countries: Uzbekistan, Kazakhstan, Tajikistan, Kyrgyzstan, and Turkmenistan. Firstly, results show common issues: neutral tones are produced overly stressed, significant deviation and confusion occur between T2 and T3, and tone deviation coexist with relative pitch inaccuracies. Secondly, when learning different tones, learners from different countries also exhibit varied performance, with Uzbekistan and Turkmenistan learners performing worst overall, while Tajikistan learners the best. Based on these findings, the study recommends optimizing tone teaching order, focusing on modal tone exercises, and designing targeted, countryspecific instructional materials.
Emotional prosody might distort realization of Mandarin tones, challenging young children's tonal perception, especially in noise. However, visual-articulatory cues have been shown to aid tonal perception in auditory-degraded conditions. This study explored emotional prosody's influence on preschoolers' tonal perception accuracy in quiet and noisy environments, and the role of visual-articulatory cues. Ninety-four children aged 4-6 and twenty-six adult controls participated. Stimuli included tones produced in emotional utterances (happy, neutral, and angry) under quiet and noisy environments. The tonal perception task was conducted in audio-only (AO) and audiovisual (AV) conditions. Results showed in AO, child groups generally exhibited high tonal accuracy across emotions, but significantly declined in noise. In AV, 6-year-olds performed comparably in quiet and noisy environments, similar to adults, indicating benefits from audiovisual integration. These findings suggest preschoolers demonstrated robust tonal perception abilities under emotional prosodic influence, and that visual-articulatory cues facilitated Mandarin tone perception in noise for 6-year-olds.
This study compares the acoustic properties of the Standard Chinese /n/ and /l/ in different prevocalic and tonal contexts The speech data of eight Mandarin speakers show that the distinction between /n/ and /l/ primarily lies in the spectral energy distribution along the frequency scale, regardless of the contexts in which two consonants occur. While /l/ exhibits enhanced high-frequency energy, /n/ displays a more skewed spectral shape with energy concentrated in the lower frequency region. The frequency-related features of /n/ and /l/ play a relatively minor role in distinction. Generally /n/ and /l/ share similar formant patterns, but the formant values of /l/ show more variation in different vowel contexts, presumably due to more coarticulation with the following vowel. There is no apparent tonal context effect on the acoustic properties of the two coronal consonants
