Latent-based SR using MoE and frequency augmented VAE decoder

Wake-up-word(WUW)system is an emerging development in recent times. Voice interaction with systems have made life ease and aids in multi-tasking. Apple, Google, Microsoft, Amazon have developed a custom wake-word engine, which are addressed by words such as ‘Hey Siri’. ‘Ok Google’, ‘Cortana’, ‘Alexa’. Our project focuses initially only detection and response to a customized wake-up command. The wake-up command used is “GOLUMOLU”. A wake-up-word detection system search for specific word and reads the word, where it rejects all other words, phrases and sounds. WUW system needs only less memory space, low computational cost and high precision. Artificial Neural Networks(ANN) have reduced the complexity, computational time, latency, thus the efficiency of system has improved. Deep learning has improved the efficiency of automatic speech recognition(SR), where wake word detection is a subset of SR but unlike keyword spotting and voice recognition. A deep learning RNN model is used for the training of the network. RNN are specifically used in case of temporal sequence data and has the ability to process data of different length but of same dimension. For training a model, labelled dataset is needed. We generated three forms of data: golumolu, negative and background. Such that, the model learns circumspectly and attentively detects when specific word found. To start communication with system, the wake word should be delivered. The main task of WUW detection system is to detect the speech, to identify WUW words among spoken words, to check whether the word spoken in altering context.

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

Sources for our slides for the latent structure in NLP tutorial

No description available

Latent SR3 代码

The current repository includes code (Mplus input files) used the estimate Latent Growth Models (LGM), Latent Class Growth Analysis (LCGA) and Growth Mixture Models (GMM) used in the analysis "Identifying trajectory subtypes of depression using IDS-SR total scores"

No description available

No description available

Latent space fidelity bounds diffusion-based medical image super-resolution. Controlled comparison of domain-specific (MedVAE) vs. generic (SD-VAE) autoencoders across knee MRI, brain MRI, and chest X-ray.

No description available

No description available

No description available

LatentShift: Solving Catastrophic Forgetting via Hilbert-Inspired Latent Shifting (NeurIPS 2026)

No description available

cst276srs00-latentdev created by GitHub Classroom

Autonomous drone interception system — IMM-EKF tracking, APN guidance, Hungarian assignment. 88.7% SR · 13ms latency · Raspberry Pi 5 + Arduino. Architecture & methodology showcase.

Hybrid two-channel transport for games: reliable (in-order) + unreliable (low-latency) over UDP, with SR+SACK. This program is dedicated as submission for Assignment 4 CS3103.