Semi-supervised learning for acoustic model retraining: Handling speech data with noisy transcript

Madan, Abhijith; Khopkar, Ayush; Nadig, Shreekantha; Raghavan, K M Srinivasa; Eledath, Dhanya; Ramasubramanian, V.

doi:10.1109/spcom50965.2020.9179517

Cited by 1 publication

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“…Fuzzy string matching (also known as 'approximate string matching') is a technique to find strings that match a target string approximately, rather than exactly (for an overview, see Singla & Garg, 2012). Common applications are found in record linkage (Wang et al, 2011), spelling checkers, spam filters (Wei et al, 2009), and also speech recognition (Schalk & Zimmerman, 2005;Wu & Chen, 2001) and acoustic model training (Madan et al, 2020). However, it has not yet been applied to transcript accuracy assessment.…”

Section: Introductionmentioning

confidence: 99%

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Bosker

2021

Behav Res

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Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants’ raw responses for words correctly identified from the target sentence is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants’ responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https://tokensortratio.netlify.app for an online implementation.

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Section: Introductionmentioning

confidence: 99%

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Bosker

2021

Behav Res

View full text Add to dashboard Cite

show abstract

Semi-supervised learning for acoustic model retraining: Handling speech data with noisy transcript

Cited by 1 publication

References 10 publications

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

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