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

Qwen-Image has been released

submitted by /u/theivan [link] [comments]

11 hours ago

Building a Decoder-Only Transformer Model for Text Generation

This post is divided into five parts; they are: • From a Full Transformer to…

11 hours ago

Rethinking how we measure AI intelligence

Game Arena is a new, open-source platform for rigorous evaluation of AI models. It allows…

11 hours ago

Ambisonics Super-Resolution Using A Waveform-Domain Neural Network

Ambisonics is a spatial audio format describing a sound field. First-order Ambisonics (FOA) is a…

11 hours ago

Cost tracking multi-tenant model inference on Amazon Bedrock

Organizations serving multiple tenants through AI applications face a common challenge: how to track, analyze,…

11 hours ago

Optimize your cloud costs using Cloud Hub Optimization and Cost Explorer

Application owners are looking for three things when they think about optimizing cloud costs: What…

11 hours ago