On Lattice Generation for Large Vocabulary Speech Recognition
Abstract
Lattice generation is an essential feature of the decoder for many
speech recognition applications. In this paper, we first review
lattice generation methods for WFST-based decoding and describe in a
uniform formalism two established approaches for state-of-the-art
speech recognition systems: the phone pair and the N-best histories
approaches. We then present a novel optimization method,
pruned determinization followed by minimization, that produces a
deterministic minimal lattice that retains all paths within specified
weight and lattice size thresholds. Experimentally, we show that
before optimization, the phone-pair and the N-best histories
approaches each have conditions where they perform better when
evaluated on video transcription and mixed voice search and dictation
tasks. However, once this lattice optimization procedure is applied,
the phone pair approach has the lowest oracle WER for a given lattice
density by a significant margin. We further show that the pruned
determinization presented here is efficient to use during decoding
unlike classical weighted determinization from which it is derived.
Finally, we consider on-the-fly lattice rescoring in which the
lattice generation and combination with the secondary LM are done
in one step. We compare the phone pair and N-best histories
approaches for this scenario and find the former superior in our
experiments.
speech recognition applications. In this paper, we first review
lattice generation methods for WFST-based decoding and describe in a
uniform formalism two established approaches for state-of-the-art
speech recognition systems: the phone pair and the N-best histories
approaches. We then present a novel optimization method,
pruned determinization followed by minimization, that produces a
deterministic minimal lattice that retains all paths within specified
weight and lattice size thresholds. Experimentally, we show that
before optimization, the phone-pair and the N-best histories
approaches each have conditions where they perform better when
evaluated on video transcription and mixed voice search and dictation
tasks. However, once this lattice optimization procedure is applied,
the phone pair approach has the lowest oracle WER for a given lattice
density by a significant margin. We further show that the pruned
determinization presented here is efficient to use during decoding
unlike classical weighted determinization from which it is derived.
Finally, we consider on-the-fly lattice rescoring in which the
lattice generation and combination with the secondary LM are done
in one step. We compare the phone pair and N-best histories
approaches for this scenario and find the former superior in our
experiments.
Research Areas
