paper-with-me

홈 › Papers

Generative Voice Bursts during Phone Call

2025-06-09 · Paritosh Ranjan, Surajit Majumder, Prodip Roy

In critical situations, conventional mobile telephony fails to convey emergency voice messages to a callee already engaged in another call. The standard call waiting alert does not provide the urgency or content of the waiting call. This paper proposes a novel method for transmitting Generative Voice Bursts short, context aware audio messages during ongoing calls, from either preauthorized or dynamically prioritized callers. By leveraging generative AI techniques, the system automatically generates spoken messages from contextual inputs example like location, health data, images, background noise when the caller is unable to speak due to incapacitation or environmental constraints. The solution incorporates voice, text, and priority inference mechanisms, allowing high priority emergency messages to bypass conventional call waiting barriers. The approach employs models such as GPT Neo for generative text, which is synthesized into audio and delivered in configurable intervals G seconds and counts N times, ensuring minimal disruption while preserving urgency. This method holds potential for significant impact across telecom, mobile device manufacturing, and emergency communication platforms.

📄 PDF Abstract BibTeX arXiv:2506.07526

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Refunds@Expedia|||How do I get a full refund from Expedia? “How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Quick Help &…
Cosine Annealing Cosine Annealing is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before…
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Linear Warmup With Cosine Annealing Linear Warmup With Cosine Annealing is a learning rate schedule where we increase the learning rate linearly for $n$ updates and then anneal according to a cosine schedule…
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Discriminative Fine-Tuning Discriminative Fine-Tuning is a fine-tuning strategy that is used for ULMFiT type models. Instead of using the same learning rate…
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Similar Papers 제목 키워드 기반

The Hidden Dance of Phonemes and Visage: Unveiling the Enigmatic Link between Phonemes and Facial Features

2023-07-26 · Liao Qu, Xianwei Zou, Xiang Li, Yandong Wen 외

This work unveils the enigmatic link between phonemes and facial features. Traditional studies on voice-face correlations typically involve using a long period of voice input, including generating face images from voices…

MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation

2026-07-29 · Wei-Jaw Lee, Hsuan-Yu Yeh, Ting-Yi Hu, Chih-Pin Tan 외 arxiv

Cover song generation (CSG) should preserve the melodic and linguistic content of a reference song while recreating the remaining musical components. The state-of-the-art model SongEcho utilizes $F_0$ sequences and voice…

Toward noise-robust whisper keyword spotting on headphones with in-earcup microphone and curriculum learning

2025-02-01 · Qiaoyu Yang, Shuo Zhang, Chuan-Che Huang

The expanding feature set of modern headphones puts a challenge on the design of their control interface. Users may want to separately control each feature or quickly switch between modes that activate different features…

Keyword Spotting

Multi-Microphone Noise Data Augmentation for DNN-based Own Voice Reconstruction for Hearables in Noisy Environments

2023-12-14 · Mattes Ohlenbusch, Christian Rollwage, Simon Doclo

Hearables with integrated microphones may offer communication benefits in noisy working environments, e.g. by transmitting the recorded own voice of the user. Systems aiming at reconstructing the clean and full-bandwidth…

Data Augmentation

Self-Supervised Burst Super-Resolution

2023-01-01 · ICCV 2023 1 · Goutam Bhat, Michaël Gharbi, Jiawen Chen, Luc van Gool 외

We introduce a self-supervised training strategy for burst super-resolution that only uses noisy low-resolution bursts during training. Our approach eliminates the need to carefully tune synthetic data simulation pip…

Super-Resolution