Name
Abstract | ||
|---|---|---|
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 Leven-shtein 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683722 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Transcription error detection, model selection, HMM-GMM, Variational Auto-Encoder, detection error tradeoff | TIMIT,Bayesian inference,Pattern recognition,Computer science,Word error rate,Model selection,Levenshtein distance,Artificial intelligence,Hidden Markov model,Language model,Bayesian probability | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jinyi Yang | 1 | 0 | 1.35 |
| Lucas Ondel | 2 | 35 | 7.16 |
| Vimal Manohar | 3 | 54 | 7.99 |
| Hynek Hermansky | 4 | 3298 | 510.27 |