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Introducing Bed Word: a new automated speech recognition tool for sociolinguistic interview transcription

  • Marcus Ma EMAIL logo , Lelia Glass and James Stanford
Published/Copyright: May 7, 2024
Linguistics Vanguard
From the journal Linguistics Vanguard Volume 10 Issue 1

Abstract

We present Bed Word, a tool leveraging industrial automatic speech recognition (ASR) to transcribe sociophonetic data. While we find lower accuracy for minoritized English varieties, the resulting vowel measurements are overall very close to those derived from human-corrected gold data, so fully automated transcription may be suitable for some research purposes. For purposes requiring greater accuracy, we present a pipeline for human post-editing of automatically generated drafts, which we show is far faster than transcribing from scratch. Thus, we offer two ways to leverage ASR in sociolinguistic research: full automation and human post-editing. Augmenting the DARLA tool developed by Reddy and Stanford (2015b. Toward completely automated vowel extraction: Introducing DARLA. Linguistics Vanguard 1(1). 15–28), we hope that this resource can help speed up transcription for sociophonetic research.

Keywords: automated transcription; automated speech recognition; linguistics; sociophonetics
Corresponding author: Marcus Ma, Georgia Institute of Technology, 564 Centennial Olympic Park Dr NW, Atlanta, GA, 30313, USA, E-mail:

Acknowledgments

We express gratitude to our speakers from the Roswell Voices project, Atlanta Speech Project, and Georgia Tech. We thank Sravana Reddy for her knowledge of and help with the DARLA system. We are indebted to the Georgia Tech VIP Team, Language and Identity in the New South, for inspiration and testing of Bed Word. Finally, we thank Joseph A. Stanley, Margaret Renwick, and Jon Forrest for invaluable feedback during the development of Bed Word.

Appendix

Regression coefficients for word error rate model

As described above, we ran a linear regression predicting each speaker’s WER as a function of their gender (male or female), ethnicity (Black or White), and the age of the data (legacy or recent), as well as the only interaction that improved the model as measured by the Akaike information criterion: an interaction between data age and ethnicity (9).

(9)
lm(WER ∼ DataAge * Ethnicity + Gender, data = wer)

The full model output is given in Table 3.

Table 3:

Output of word error rate regression.

Estimate SE t value Pr(>|t|)
(Intercept) 0.28 0.04 6.41 2.21e−07***
DataAgeLegacy −0.02 0.06 −0.39 0.70
EthnicityBlack 0.01 0.06 0.24 0.81
GenderM 0.01 0.04 0.17 0.87
DataAgeLegacy:EthnicityBlack 0.32 0.08 4.04 0.0003***

Comparison of available industrial ASR models

To determine the industry model behind Bed Word, we surveyed the ASR models from Amazon, Google, Microsoft, and Deepgram. We evaluated WER on the same Georgia corpus used throughout the study (Table 4).

Table 4:

Evaluations of ASR models on our Georgia speaker corpus.

Amazon Google Microsoft Deepgram
Word error rate 0.562 0.555 0.564 0.375
Cost per audio hour $0.96 $1.44 $1.00 $0.25
Ease of use (subjective) Medium Easy Hard Easy

Overall, we judge Deepgram as superior to the other three models.

Word error rates by vowel type

Here, we present the WER for Atl_002 broken down by vowel type. We manually compared the gold handwritten transcription with Bed Word’s silver auto-generated transcription and noted transcription errors (substitutions, deletions, and insertions) where the vowel type is mistaken. For example, if school was mistranscribed as skull, that would count as an error for the goose vowel (mischaracterizing it as the strut vowel), while mistranscribing state as of steak does not count as an error (because both share the same face vowel). In Figure 5, we see that overall the percentage of errors for each vowel type closely mirrors its overall frequency, meaning that Bed Word is likely not biased towards transcribing certain vowels with more errors than others.

Figure 5: For each of the top 12 most frequent vowel types, its percentage of all vowel tokens, and its percentage of all vowel-mistaking transcription, for Atl_002.
Figure 5:

For each of the top 12 most frequent vowel types, its percentage of all vowel tokens, and its percentage of all vowel-mistaking transcription, for Atl_002.

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Received: 2023-05-18
Accepted: 2023-09-28
Published Online: 2024-05-07

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/lingvan-2023-0073
Keywords for this article
automated transcription; automated speech recognition; linguistics; sociophonetics

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  1. Frontmatter
  2. Editorial
  3. Editorial 2024
  4. Phonetics & Phonology
  5. The role of recoverability in the implementation of non-phonemic glottalization in Hawaiian
  6. Epenthetic vowel quality crosslinguistically, with focus on Modern Hebrew
  7. Japanese speakers can infer specific sub-lexicons using phonotactic cues
  8. Articulatory phonetics in the market: combining public engagement with ultrasound data collection
  9. Investigating the acoustic fidelity of vowels across remote recording methods
  10. The role of coarticulatory tonal information in Cantonese spoken word recognition: an eye-tracking study
  11. Tracking phonological regularities: exploring the influence of learning mode and regularity locus in adult phonological learning
  12. Morphology & Syntax
  13. #AreHashtagsWords? Structure, position, and syntactic integration of hashtags in (English) tweets
  14. The meaning of morphomes: distributional semantics of Spanish stem alternations
  15. A refinement of the analysis of the resultative V-de construction in Mandarin Chinese
  16. L2 cognitive construal and morphosyntactic acquisition of pseudo-passive constructions
  17. Semantics & Pragmatics
  18. “All women are like that”: an overview of linguistic deindividualization and dehumanization of women in the incelosphere
  19. Counterfactual language, emotion, and perspective: a sentence completion study during the COVID-19 pandemic
  20. Constructing elderly patients’ agency through conversational storytelling
  21. Language Documentation & Typology
  22. Conative animal calls in Macha Oromo: function and form
  23. The syntax of African American English borrowings in the Louisiana Creole tense-mood-aspect system
  24. Syntactic pausing? Re-examining the associations
  25. Bibliographic bias and information-density sampling
  26. Historical & Comparative Linguistics
  27. Revisiting the hypothesis of ideophones as windows to language evolution
  28. Verifying the morpho-semantics of aspect via typological homogeneity
  29. Psycholinguistics & Neurolinguistics
  30. Sign recognition: the effect of parameters and features in sign mispronunciations
  31. Influence of translation on perceived metaphor features: quality, aptness, metaphoricity, and familiarity
  32. Effects of grammatical gender on gender inferences: Evidence from French hybrid nouns
  33. Processing reflexives in adjunct control: an exploration of attraction effects
  34. Language Acquisition & Language Learning
  35. How do L1 glosses affect EFL learners’ reading comprehension performance? An eye-tracking study
  36. Modeling L2 motivation change and its predictive effects on learning behaviors in the extramural digital context: a quantitative investigation in China
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  51. Shifts in digital media usage before and after the pandemic by Rusyns in Ukraine
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  57. Through the compression glass: language complexity and the linguistic structure of compressed strings
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  62. Introducing Bed Word: a new automated speech recognition tool for sociolinguistic interview transcription
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  64. Enhancing automated essay scoring with GCNs and multi-level features for robust multidimensional assessments
  65. Sociolinguistic auto-coding has fairness problems too: measuring and mitigating bias
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