Table 1.

Explanation of technical terms

Term	Description	Reference(s)
Beam search	A heuristic search algorithm. In Chiron, the beam search decoder with beam width W maintains a list of the W most probable sequences up to position i and constructs the probabilities of all possible sequence extensions for i + 1.	[32]
Connectionist Temporal Classification (CTC) decoder	A type of neural network output and scoring for labeling sequence data with RNNs. It does not require presegmented training data and postprocessed outputs.	[56]
Convolutional Neural Network (CNN)	A type of neural network often used for image analysis. It can recognize patterns by applying different filters to an image.	[57]
Forward algorithm	An algorithm that computes the probability P(x) of a sequence x given a certain HMM.	[58]
Hidden Markov Model (HMM)	A stochastic model that models a sequence of unobserved events underlying a sequence of observations. HMMs assume that an event only depends on the previous event.	[58, 59]
Long-short-term memory (LSTM) unit	A type of RNN that can be used as a building block in bigger networks. It has specific input, output, and forgot gates that allow it to retain or discard information that was passed on from a previous state.	[60, 61]
Partial Order Alignment (POA) graph	A graph representation of a multiple alignment that allows each base in the alignment to have multiple predecessors. Different paths through the graph represent different alignments.	[62]
Recurrent Neural Network (RNN)	A type of neural network that takes information passed on from previous states into account.	[63]
Training data	A dataset that is used to optimize (i.e., train) the parameters of a model. Training is required for both HMMs and RNNs. The training dataset thus determines the performance of the model.	[58, 63]
Viterbi decoding	An algorithm that finds the most likely sequence of events given a certain HMM.	[58]