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cs.SD updates on arXiv.org

Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost Korean aegyo speech shows systematic F1 increase to signal childlike qualities All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation RAS: a Reliability Oriented Metric for Automatic Speech Recognition Speech Enhancement Based on Drifting Models HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition Materialistic RIR: Material Conditioned Realistic RIR Generation SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR ATIR: Towards Audio-Text Interleaved Contextual Retrieval Enhancing Speaker Verification with Whispered Speech via Post-Processing Environmental Sound Deepfake Detection Using Deep-Learning Framework Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India Tadabur: A Large-Scale Quran Audio Dataset
Large-Scale Acoustic Characterization of Singaporean Chil...
Yuling Gu, Nancy F. Chen · 2022-02-18 · via cs.SD updates on arXiv.org

In this work, we investigate pronunciation differences in English spoken by Singaporean children in relation to their American and British counterparts by conducting Kmeans clustering and Archetypal analysis on selected vowel pairs and approximants. Given that Singapore adopts British English as the institutional standard due to historical reasons, one might expect Singaporean children to follow British pronunciation patterns. Indeed, Singaporean and British children are more similar in their production of syllable-final /r/ -- they do not lower their third formant nearly as much as American children do, suggesting a lack of rhoticity. Interestingly, Singaporean children also present similar patterns to American children when it comes to their fronting of vowels as demonstrated across various vowels including TRAP-BATH split vowels. Singaporean children's English also demonstrated characteristics that do not resemble any of the other two populations. We observe that Singaporean children's vowel height characteristics are distinct from both that of American and British children. In tense and lax vowel pairs, we also consistently observe that the distinction is less conspicuous for Singaporean children compared to the other speaker groups. Further, while American and British children demonstrate lowering of F1 and F2 formants in transitions into syllable-final /l/s, a wide gap between F2 and F3 formants, and small difference between F1 and F2 formants, all of these are not exhibited in Singaporean children's pronunciation. These findings point towards potential sociolinguistic implications of how Singapore English might be evolving to embody more than British pronunciation characteristics. Furthermore, these findings also suggest that Singapore English could be have been influenced by languages beyond American and British English, potentially due to Singapore's multilingual environment.