DNN-Based Semantic Model for Rescoring N-best Speech Recognition List

TL;DR

This paper introduces a DNN-based semantic rescoring method for N-best speech recognition hypotheses, utilizing word embeddings and acoustic features to reduce word error rate under noisy conditions.

Contribution

It proposes a novel DNN model that incorporates semantic and acoustic features for rescoring, improving speech recognition accuracy in noisy environments.

Findings

Significant WER reduction in noisy conditions

Effective use of word2vec and BERT embeddings

Improved performance over baseline models

Abstract

The word error rate (WER) of an automatic speech recognition (ASR) system increases when a mismatch occurs between the training and the testing conditions due to the noise, etc. In this case, the acoustic information can be less reliable. This work aims to improve ASR by modeling long-term semantic relations to compensate for distorted acoustic features. We propose to perform this through rescoring of the ASR N-best hypotheses list. To achieve this, we train a deep neural network (DNN). Our DNN rescoring model is aimed at selecting hypotheses that have better semantic consistency and therefore lower WER. We investigate two types of representations as part of input features to our DNN model: static word embeddings (from word2vec) and dynamic contextual embeddings (from BERT). Acoustic and linguistic features are also included. We perform experiments on the publicly available dataset TED-LIUM mixed with real noise. The proposed rescoring approaches give significant improvement of the WER over the ASR system without rescoring models in two noisy conditions and with n-gram and RNNLM.