Wednesday, November 6, 2024

Artificial Intelligence news

How ChatGPT search paves...

This story originally appeared in The Algorithm, our weekly newsletter on AI....

This AI-generated Minecraft may...

When you walk around in a version of the video game Minecraft...

OpenAI brings a new...

ChatGPT can now search the web for up-to-date answers to a user’s...

Chasing AI’s value in...

Inspired by an unprecedented opportunity, the life sciences sector has gone all...
HomeMachine LearningMatching Latent Encoding...

Matching Latent Encoding for Audio-Text based Keyword Spotting



Using audio and text embeddings jointly for Keyword Spotting (KWS) has shown high-quality results, but the key challenge of how to semantically align two embeddings for multi-word keywords of different sequence lengths remains largely unsolved. In this paper, we propose an audio-text-based end-to-end model architecture for flexible keyword spotting (KWS), which builds upon learned audio and text embeddings. Our architecture uses a novel dynamic programming-based algorithm, Dynamic Sequence Partitioning (DSP), to optimally partition the audio sequence into the same length as the…



Article Source link and Credit

Continue reading

Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs

Translating text that contains entity names is a challenging task, as cultural-related references can vary significantly across languages. These variations may also be caused by transcreation, an adaptation process that entails more than transliteration and word-for-word translation. In...

ConvKGYarn: Spinning Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models

The rapid evolution of Large Language Models (LLMs) and conversational assistants necessitates dynamic, scalable, and configurable conversational datasets for training and evaluation. These datasets must accommodate diverse user interaction modes, including text and voice, each presenting unique modeling...

Promoting Cross-Modal Representations to Improve Multimodal Foundation Models for Physiological Signals

Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods for multimodal healthcare data is crucial. Pretraining foundation models is a promising avenue for success. However,...