Categories: FAANG

A Treatise On FST Lattice Based MMI Training

Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models. This paper aims to isolate, identify and bring forward the implicit modelling decisions induced by the design implementation of standard finite state transducer (FST) lattice based MMI training framework. The paper particularly investigates the necessity to maintain a preselected numerator alignment and raises the importance of determinizing FST denominator lattices on the fly. The efficacy of employing on the fly FST lattice determinization is…
AI Generated Robotic Content

Recent Posts

AI, Light, and Shadow: Jasper’s New Research Powers More Realistic Imagery

Jasper Research Lab’s new shadow generation research and model enable brands to create more photorealistic…

7 hours ago

Gemini 2.0 is now available to everyone

We’re announcing new updates to Gemini 2.0 Flash, plus introducing Gemini 2.0 Flash-Lite and Gemini…

7 hours ago

Reinforcement Learning for Long-Horizon Interactive LLM Agents

Interactive digital agents (IDAs) leverage APIs of stateful digital environments to perform tasks in response…

7 hours ago

Trellix lowers cost, increases speed, and adds delivery flexibility with cost-effective and performant Amazon Nova Micro and Amazon Nova Lite models

This post is co-written with Martin Holste from Trellix.  Security teams are dealing with an…

7 hours ago

Designing sustainable AI: A deep dive into TPU efficiency and lifecycle emissions

As AI continues to unlock new opportunities for business growth and societal benefits, we’re working…

7 hours ago

NOAA Employees Told to Pause Work With ‘Foreign Nationals’

An internal email obtained by WIRED shows that NOAA workers received orders to pause “ALL…

8 hours ago