Exploring A Zero-Order Direct Hmm Based on Latent Attention for Automatic Speech Recognition

IEEE International Conference on Acoustics Speech and Signal Processing

Abstract

In this paper, we study a simple yet elegant latent variable attention model for automatic speech recognition (ASR) which enables an integration of attention sequence modeling into the direct hidden Markov model (HMM) concept. We use a sequence of hidden variables that establishes a mapping from output labels to input frames. Inspired by the direct HMM model, we assume a decomposition of the label sequence posterior into emission and transition probabilities using zero-order assumption and incorporate both Transformer and LSTM attention models into it. The method keeps the explicit alignment as part of the stochastic model and combines the ease of the end-to-end training of the attention model as well as an efficient and simple beam search. To study the effect of the latent model, we qualitatively analyze the alignment behavior of the different approaches. Our experiments on three ASR tasks show promising results in WER with more focused alignments in comparison to the attention models.

Authors 5

1. Parnia Bahar Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany

2. Nikita Makarov Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany

3. Albert Zeyer Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany

4. Ralf Schlüter Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany

5. Hermann Ney Aachen Human Language Technology and Pattern Recognition Group

   RWTH Aachen University

   Affiliation as printed

   Human Language Technology and Pattern Recognition Group, RWTH Aachen University, Aachen, Germany