Detail publikace

Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings

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

Originální název

Towards Automatic Methods to Detect Errors in Transcriptions of Speech Recordings

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

This work explores different methods to detect errors in transcriptionsof speech recordings. We artificially corrupt well transcribedspeech transcriptions with three types of errors: substitution, insertionand deletion on TIMIT phonemic transcriptions and WSJ wordtranscriptions. First, we use Bayesian model selection method bycomparing the log-likelihoods from alignment and phone recognizer,a final score is computed to make decision. In this method, weconsider two models, Bayesian Hidden Markov Model (HMM) anda Variational Auto-Encoder (VAE) combined with a HMM. Alternately,we build a biased ASR system with language models trainedon individual transcriptions, detection decision is based on Levenshteindistance (LD) between transcription and oracle path from decodedlattice. We evaluate the methods of detecting errors in corruptedTIMIT transcription, the best result (either using model selectionwith VAE model or biased ASR) achieves 7% equal errorrate on the Detection Error Tradeoff (DET) curve; we also evaluatethe methods of detecting errors in corrupted WSJ transcriptions, andthe best result (using biased ASR) achieves 3% equal error rate.

Klíčová slova

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

Autoři

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

Vydáno

12. 5. 2019

Nakladatel

IEEE Signal Processing Society

Místo

Brighton

ISBN

978-1-5386-4658-8

Kniha

Proceedings of ICASSP

Strany od

3747

Strany do

3751

Strany počet

5

URL

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

BibTex

@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"
}

Dokumenty

yang_icassp2019_0003747.pdf