A Parallelizable Lattice Rescoring Strategy with Neural Language Models

• URL: http://arxiv.org/abs/2103.05081v1
• Date: Mon, 8 Mar 2021 21:23:12 GMT
• Title: A Parallelizable Lattice Rescoring Strategy with Neural Language Models
• Authors: Ke Li, Daniel Povey, Sanjeev Khudanpur
• Abstract summary: A posterior-based lattice expansion algorithm is proposed for efficient lattice rescoring with neural language models (LMs) for automatic speech recognition. Experiments on the Switchboard dataset show that the proposed rescoring strategy obtains comparable recognition performance. The parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.
• Score: 62.20538383769179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a parallel computation strategy and a posterior-based lattice expansion algorithm for efficient lattice rescoring with neural language models (LMs) for automatic speech recognition. First, lattices from first-pass decoding are expanded by the proposed posterior-based lattice expansion algorithm. Second, each expanded lattice is converted into a minimal list of hypotheses that covers every arc. Each hypothesis is constrained to be the best path for at least one arc it includes. For each lattice, the neural LM scores of the minimal list are computed in parallel and are then integrated back to the lattice in the rescoring stage. Experiments on the Switchboard dataset show that the proposed rescoring strategy obtains comparable recognition performance and generates more compact lattices than a competitive baseline method. Furthermore, the parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.

Related papers

• Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks [15.074950361970194]
  We provide a unified analysis for a family of algorithms that encompasses IRLS, the recently proposed linlin-RFM algorithm, and the alternating diagonal neural networks. We show that, with appropriately chosen reweighting policy, a handful of sparse structures can achieve favorable performance. We also show that leveraging this in the reweighting scheme provably improves test error compared to coordinate-wise reweighting.
  arXiv  Detail & Related papers  (2024-06-04T20:37:17Z)
• Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach [14.536819369925398]
  We design a deep neural model outputting factorized unimodular matrices and train it in a self-supervised manner by penalizing non-orthogonal lattice bases.
  arXiv  Detail & Related papers  (2023-11-14T13:54:35Z)
• A Unified Algebraic Perspective on Lipschitz Neural Networks [88.14073994459586]
  This paper introduces a novel perspective unifying various types of 1-Lipschitz neural networks. We show that many existing techniques can be derived and generalized via finding analytical solutions of a common semidefinite programming (SDP) condition. Our approach, called SDP-based Lipschitz Layers (SLL), allows us to design non-trivial yet efficient generalization of convex potential layers.
  arXiv  Detail & Related papers  (2023-03-06T14:31:09Z)
• A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms [64.3064050603721]
  We generalize Runge-Kutta neural network to a recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms. We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields similar iterations to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta solvers for ordinary differential equations.
  arXiv  Detail & Related papers  (2022-11-22T16:30:33Z)
• Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation [64.49871502193477]
  We propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. Comprehensive experimental results on six commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-21T01:47:17Z)
• Unfolding Projection-free SDP Relaxation of Binary Graph Classifier via GDPA Linearization [59.87663954467815]
  Algorithm unfolding creates an interpretable and parsimonious neural network architecture by implementing each iteration of a model-based algorithm as a neural layer. In this paper, leveraging a recent linear algebraic theorem called Gershgorin disc perfect alignment (GDPA), we unroll a projection-free algorithm for semi-definite programming relaxation (SDR) of a binary graph. Experimental results show that our unrolled network outperformed pure model-based graph classifiers, and achieved comparable performance to pure data-driven networks but using far fewer parameters.
  arXiv  Detail & Related papers  (2021-09-10T07:01:15Z)
• Coordinate descent on the orthogonal group for recurrent neural network training [9.886326127330337]
  We show that the algorithm rotates two columns of the recurrent matrix, an operation that can be efficiently implemented as a multiplication by a Givens matrix. Experiments on a benchmark recurrent neural network training problem are presented to demonstrate the effectiveness of the proposed algorithm.
  arXiv  Detail & Related papers  (2021-07-30T19:27:11Z)
• Partition-based formulations for mixed-integer optimization of trained ReLU neural networks [66.88252321870085]
  This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. At one extreme, one partition per input recovers the convex hull of a node, i.e., the tightest possible formulation for each node.
  arXiv  Detail & Related papers  (2021-02-08T17:27:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.