Publication Details
Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings
Transcription error detection, model selection, HMM-GMM, Variational
Auto-Encoder, detection error tradeoff
This work explores different methods to detect errors in transcriptions of speech
recordings. We artificially corrupt well transcribed speech transcriptions with
three types of errors: substitution, insertion and deletion on TIMIT phonemic
transcriptions and WSJ word transcriptions. First, we use Bayesian model
selection method by comparing the log-likelihoods from alignment and phone
recognizer, a final score is computed to make decision. In this method, we
consider two models, Bayesian Hidden Markov Model (HMM) and a Variational
Auto-Encoder (VAE) combined with a HMM. Alternately, we build a biased ASR system
with language models trained on individual transcriptions, detection decision is
based on Levenshtein distance (LD) between transcription and oracle path from
decoded lattice. We evaluate the methods of detecting errors in corrupted TIMIT
transcription, the best result (either using model selection with VAE model or
biased ASR) achieves 7% equal error rate on the Detection Error Tradeoff (DET)
curve; we also evaluate the methods of detecting errors in corrupted WSJ
transcriptions, and the best result (using biased ASR) achieves 3% equal error
rate.
@inproceedings{BUT160007,
author="YANG, J. and ONDEL YANG, L. and MANOHAR, V. and HEŘMANSKÝ, H.",
title="Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings",
booktitle="Proceedings of ICASSP",
year="2019",
pages="3747--3751",
publisher="IEEE Signal Processing Society",
address="Brighton",
doi="10.1109/ICASSP.2019.8683722",
isbn="978-1-5386-4658-8",
url="https://ieeexplore.ieee.org/document/8683722"
}