PSRB, a 10.4-hour Persian benchmark built from 3,372 clips and 756 speakers, evaluates ten ASR models and introduces SW-WER, showing that systems are far weaker on regional accents, children's speech, and informal audio than on standard adult read speech.
Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio
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abstract
Hallucinations of deep neural models are amongst key challenges in automatic speech recognition (ASR). In this paper, we investigate hallucinations of the Whisper ASR model induced by non-speech audio segments present during inference. By inducting hallucinations with various types of sounds, we show that there exists a set of hallucinations that appear frequently. We then study hallucinations caused by the augmentation of speech with such sounds. Finally, we describe the creation of a bag of hallucinations (BoH) that allows to remove the effect of hallucinations through the post-processing of text transcriptions. The results of our experiments show that such post-processing is capable of reducing word error rate (WER) and acts as a good safeguard against problematic hallucinations.
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PSRB: A Comprehensive Benchmark for Evaluating Persian ASR Systems
PSRB, a 10.4-hour Persian benchmark built from 3,372 clips and 756 speakers, evaluates ten ASR models and introduces SW-WER, showing that systems are far weaker on regional accents, children's speech, and informal audio than on standard adult read speech.