Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings

conference paper

English

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.

Transcription error detection, model selection, HMM-GMM, Variational Auto-Encoder, detection error tradeoff

YANG, J.; ONDEL YANG, L.; MANOHAR, V.; HEŘMANSKÝ, H.

12. 5. 2019

IEEE Signal Processing Society

Brighton

978-1-5386-4658-8

Proceedings of ICASSP

3747

3751

5

https://ieeexplore.ieee.org/document/8683722

yang_icassp2019_0003747.pdf