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Automatic Transcription of Speech Supporting Code Switching
 Bílek, Štěpán ; Karafiát, Martin (referee) ; Szőke, Igor (advisor)
This thesis addresses the issue of automatic speech recognition, focusing on the recognition of audio containing multilingual speech, known as code-switching. The problem of a lack of multilingual data for training is addressed by combining recordings in English and German. To achieve the closest approximation to real bilingual speech, a portion of the datasets is created by merging recordings of similar speakers. The Whisper model is trained and tested on the created data. In its original unadapted version, the model achieves an error rate of up to 70 %. The best models trained on combined datasets achieve error rates slightly above 7 %. The results of this study demonstrate methods for training models to achieve the best possible performance.
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