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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
Development of the Listening in Spatialized Noise-Sentenc...
Bruno S. Masiero, Leticia R. Borges, Harvey Dillon, Maria Franci · 2024-09-06 · via cs.SD updates on arXiv.org

The Listening in Spatialized Noise Sentences (LiSN-S) is a test to evaluate auditory spatial processing currently only available in the English language. It produces a three-dimensional auditory environment under headphones and uses a simple repetition response protocol to determine speech reception thresholds (SRTs) for sentences presented in competing speech under various conditions. In order to develop the LiSN-S test in Brazilian Portuguese, it was necessary to prepare a speech database recorded by professional voice actresses and to devise presentation software. These sentences were presented to 35 adults (aged between 19 and 40 years) and 24 children (aged between 8 and 10 years), all with normal hearing-verified through tone and speech audiometry and tympanometry-and good performance at school. We used a logistic curve describing word error rate versus presentation level, fitted for each sentence, to select a set of 120 sentences for the test. Furthermore, all selected sentences were adjusted in amplitude for equal intelligibility. The framework of LiSN-S in Brazilian Portuguese is ready for normative data analysis. After its conclusion, we believe it will contribute to diagnosing and rehabilitating Brazilian children with complaints related to hearing difficulties in noisy environments