## What is lattice in kaldi?

A lattice is a representation of the alternative word-sequences that are “sufficiently likely” for a particular utterance. In order to understand lattices properly you have to understand decoding graphs in the WFST framework (see Decoding graph construction in Kaldi). There are two representations of lattices in Kaldi.

### What is lattice in speech recognition?

A speech recognition lattice is a weighted directed acyclic graph where each path from the start state to a final state represents an alternative transcription hypothesis, weighted by its recognition score for a given utterance [1].

#### What is lattice rescoring?

The lattice rescoring is a form of multi-pass decoding, in which the lattice is generated in the first pass using simple and low order knowledge sources and the rescoring is performed in the second pass using higher order knowledge sources.

**What is a rescoring algorithm?**

Lattice-rescoring is a common approach to take advantage of recurrent neural language models in ASR, where a word-lattice is generated from 1st-pass decoding and the lattice is then rescored with a neural model, and an n -gram approximation method is usually adopted to limit the search space.

**What is rescoring in ASR?**

Abstract: End-to-end approaches for automatic speech recognition (ASR) benefit from directly modeling the probability of the word sequence given the input audio stream in a single neural network.

## What is LM rescoring?

ABSTRACT. Lattice-rescoring is a common approach to take advantage of recurrent neural language models in ASR, where a word- lattice is generated from 1st-pass decoding and the lattice is then rescored with a neural model, and an n-gram ap- proximation method is usually adopted to limit the search space.

### What is rescoring in kaldi?

Lattice-rescoring is a common approach to take advantage of recurrent neural language models in ASR, where a word- lattice is generated from 1st-pass decoding and the lattice is then rescored with a neural model, and an n-gram ap- proximation method is usually adopted to limit the search space.

#### What do you mean by lattice in Kaldi?

Lattices in Kaldi. A lattice is a representation of the alternative word-sequences that are “sufficiently likely” for a particular utterance. In order to understand lattices properly you have to understand decoding graphs in the WFST framework (see Decoding graph construction in Kaldi).

**What are the Max states of kaldi.lat?**

This is helpful in order to prevent ‘pathological’ lattices from causing the program to exhaust memory. Actual max-states is 1000 + max-expand * orig-num-states.

**How to calculate the gradient in Kaldi lattice free MMI?**

Here, the term logpθ(ot ∣ st) is score that is usually output by a neural network, so the corresponding gradient is simply done during backpropagation. For the gradient of the overall objective, we multiply this with the term γrt(s ∣ Wr) − γrt(s). The key then is to compute the state occupancies for the numerator term and the denominator term.

## What do floating point weights represent in Kaldi?

This is an FST whose weight type contains two floating point weights (the graph cost and the acoustic cost), whose input symbols are transition-ids (these are roughly like context-dependent HMM states), and whose output symbols typically represent words (in general, they represent whatever the output symbols on your decoding graph represented).