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A multi-threaded approach for improved and faster accent transcription of chemical terms

Sonali Kothari

Sonali Kothari

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Kothari, Sonali
, 
Shwetambari Chiwhane

Shwetambari Chiwhane

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Chiwhane, Shwetambari
, 
Shreeja Mehta

Shreeja Mehta

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Mehta, Shreeja
, 
Pranav Naranatt

Pranav Naranatt

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Naranatt, Pranav
, 
Md. Asad Ansari

Md. Asad Ansari

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Ansari, Md. Asad
 and 
Rithwik Satya

Rithwik Satya

Department of Computer Science and Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University)Pune, India
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Satya, Rithwik
  
Apr 25, 2025
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Article Category: Research article
Published Online: Apr 25, 2025
Received: Feb 05, 2025
DOI: https://doi.org/10.2478/ijssis-2025-0016
Keywords
© 2025 Sonali Kothari et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Overview of the proposed work.
Overview of the proposed work.

Figure 2:

Initial model.
Initial model.

Figure 3:

Flow diagram of improved model.
Flow diagram of improved model.

Figure 4:

Improved model.
Improved model.

Figure 5:

Comparison performance (in seconds).
Comparison performance (in seconds).

Figure 6:

First meaningful transcription time.
First meaningful transcription time.

Figure 7:

Stress testing (hours).
Stress testing (hours).

Figure 8:

WER scores without noise. WER, word error rate.
WER scores without noise. WER, word error rate.

Figure 9:

WER scores with noise. WER, word error rate.
WER scores with noise. WER, word error rate.

Figure 10:

Time taken for transcription.
Time taken for transcription.

Figure 11:

WER comparison with Google-STT. WER, word error rate; STT, Speech-to-Text.
WER comparison with Google-STT. WER, word error rate; STT, Speech-to-Text.

Figure 12:

Time taken for transcription comparison with Google STT. STT, Speech-to-Text.
Time taken for transcription comparison with Google STT. STT, Speech-to-Text.

Figure 13:

Confusion matrix for classification of chemical elements from text.
Confusion matrix for classification of chemical elements from text.

Figure 14:

Web application.
Web application.

Figure 15:

Email details.
Email details.

Comparative results (in seconds)

Audio file Audio duration Initial model Improved model
audio 001 38.15 44.80 40.83
audio 002 70.97 79.53 79.83
audio 003 80.69 87.78 82.72
audio 004 54.86 62.19 59.21
audio 005 33.25 38.09 39.40
audio 006 40.93 58.66 53.68
audio 007 48.13 53.85 51.81
audio 008 33.49 38.68 35.13
audio 009 33.94 38.55 33.82
audio 010 48.95 54.28 50.15

Performance of existing AER systems over Indian accents

Feature Whisper (OpenAI) [16] Wav2Vec2 (Meta) [17] Google STT [18]
Indian Accent Support Strong (multilingual model trained on diverse accents) [19,20] Varies (depends on fine-tuned dataset) [20] Good (Google has extensive Indian English training data) [21]
Regional Variants (Hindi-English, Tamil-English, etc.) Handles code-switching well [22] Requires specific fine-tuning for mixed languages [23] Decent but struggles with heavy accents [18]
Noise Robustness Strong (performs well in real-world noisy environments) [16] Moderate (depends on fine-tuned model) [17] Good (handles background noise effectively) [18]
Spoken Speed Adaptability Good (handles fast speech well) [22] Varies (pre-trained models sometimes struggle) [23] Good (adjusts well to fast-paced speech) [18]

First meaningful transcription time (in seconds)

Audio Duration Initial model Improved model
audio 001 38.15 44.80 3.00
audio 002 70.97 79.53 5.05
audio 003 80.69 87.78 4.33
audio 004 54.86 62.19 4.35
audio 005 33.25 38.09 2.87
audio 006 40.93 58.66 6.10
audio 007 48.13 53.85 3.05
audio 008 33.49 38.68 2.73
audio 009 33.94 38.55 2.51
audio 010 48.95 54.28 3.40

Performance of existing AER systems for chemical term recognition

Feature Whisper (OpenAI) Wav2Vec2 (Meta) Google STT
Chemical Terms Recognition Limited (depends on general training data, not domain-specific) [16] Can be fine-tuned for better accuracy [17] Good (Google’s general corpus covers some scientific terms) [18]
Adaptability to Scientific Jargon Poor without custom fine-tuning [19] Can be trained on specialized datasets [20] Better but not perfect [21]
Handling of Long & Complex Terms Struggles with rare chemical names [16] Can be improved with domain-specific training [17] Sometimes recognizes common scientific terms but struggles with rare ones [18]

Stress testing (hours)

Audio Duration Initial model Improved model
long audio01 1.144 1.299 1.144
long audio02 3.027 3.363 3.029
Language:
English
Publication timeframe:
1 times per year
Journal Subjects:
Engineering, Introductions and Overviews, Engineering, other
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