惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Google DeepMind News
Google DeepMind News
Microsoft Security Blog
Microsoft Security Blog
B
Blog
Martin Fowler
Martin Fowler
WordPress大学
WordPress大学
爱范儿
爱范儿
博客园_首页
博客园 - 聂微东
量子位
V
Visual Studio Blog
aimingoo的专栏
aimingoo的专栏
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
小众软件
小众软件
大猫的无限游戏
大猫的无限游戏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
N
Netflix TechBlog - Medium
F
Fortinet All Blogs
The Cloudflare Blog
T
Tailwind CSS Blog
G
Google Developers Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
月光博客
月光博客
腾讯CDC

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
Epoch-Synchronous Overlap-Add (ESOLA) for Time- and Pitch...
Sunil Rudresh, Aditya Vasisht, Karthika Vijayan, Chandra Sekhar · 2018-01-20 · via cs.SD updates on arXiv.org

Time- and pitch-scale modifications of speech signals find important applications in speech synthesis, playback systems, voice conversion, learning/hearing aids, etc.. There is a requirement for computationally efficient and real-time implementable algorithms. In this paper, we propose a high quality and computationally efficient time- and pitch-scaling methodology based on the glottal closure instants (GCIs) or epochs in speech signals. The proposed algorithm, termed as epoch-synchronous overlap-add time/pitch-scaling (ESOLA-TS/PS), segments speech signals into overlapping short-time frames and then the adjacent frames are aligned with respect to the epochs and the frames are overlap-added to synthesize time-scale modified speech. Pitch scaling is achieved by resampling the time-scaled speech by a desired sampling factor. We also propose a concept of epoch embedding into speech signals, which facilitates the identification and time-stamping of samples corresponding to epochs and using them for time/pitch-scaling to multiple scaling factors whenever desired, thereby contributing to faster and efficient implementation. The results of perceptual evaluation tests reported in this paper indicate the superiority of ESOLA over state-of-the-art techniques. ESOLA significantly outperforms the conventional pitch synchronous overlap-add (PSOLA) techniques in terms of perceptual quality and intelligibility of the modified speech. Unlike the waveform similarity overlap-add (WSOLA) or synchronous overlap-add (SOLA) techniques, the ESOLA technique has the capability to do exact time-scaling of speech with high quality to any desired modification factor within a range of 0.5 to 2. Compared to synchronous overlap-add with fixed synthesis (SOLAFS), the ESOLA is computationally advantageous and at least three times faster.