English–Twi Code-Switching ASR Model - Kasanoma

Model Overview

This model is a fine-tuned Automatic Speech Recognition (ASR) system designed for English–Twi code-switching speech transcription. It is built on a pretrained Akan-adapted Whisper model and further fine-tuned on a curated bilingual dataset containing English, Twi, and mixed-language utterances.

The model supports natural bilingual speech, including intra-sentential and inter-sentential code-switching.

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Base Model

• GiftMark/akan-whisper-model

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Task

• Automatic Speech Recognition (ASR)
• Code-switching speech transcription
• English and Twi bilingual speech recognition

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Dataset

• Kennethdot/Ghana_English-Twi_Code-switching_ASR

The dataset contains:

• Code-switched English–Twi speech
• Monolingual English and Twi speech
• Read and semi-spontaneous utterances
• Carefully transcribed bilingual speech with preserved linguistic structure

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Evaluation Setup

Evaluation was performed using Word Error Rate (WER) without text normalization.

This means:

• No lowercasing
• No punctuation removal
• No orthographic normalization applied

WER reflects raw transcription fidelity.

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Results

Model	CS WER	Twi WER	English WER
Zero-shot Akan Whisper Small	127.08	116.08	110.26
Fine-tuned Model	6.58	99.44	100.43

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Key Findings

• Fine-tuning leads to a significant improvement in code-switching ASR performance
• The model achieves strong performance on bilingual utterances after adaptation
• Monolingual performance remains relatively unchanged, indicating limited cross-language transfer gain
• Code-switching appears to be the most learnable and most improved component of the task

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Qualitative Examples

The model is capable of producing fluent bilingual outputs with preserved punctuation and natural speech patterns:

Example 1 -- Twitwa enam no into small pieces for the light soup.

Example 2 -- just realized that w'abusua yɛ Ɔyoko, so you are royalty.

Example 3 -- Wo nim sɛ I almost forgot to buy the food?

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Limitations

• Model is sensitive to orthographic variation and punctuation
• Some degradation occurs on highly monolingual segments after fine-tuning
• Requires further balancing of training data across languages

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Intended Use

• Code-switching ASR research
• Low-resource African language speech recognition
• Bilingual speech transcription systems
• Linguistic analysis of English–Twi speech patterns

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Ethical Considerations

• The model is intended for research and educational use only
• It should not be used for surveillance or unauthorized speech monitoring
• Bias may exist due to dataset imbalance between languages