Seeing isn't Hearing: Benchmarking Vision Language Models at Interpreting Spectrograms
This work addresses the problem of evaluating VLMs' ability to act as phoneticians for speech interpretation, which is incremental as it applies existing methods to a new domain.
The paper benchmarks vision-language models (VLMs) on interpreting spectrograms and waveforms of speech, finding that both zero-shot and finetuned models rarely perform above chance on a multiple-choice task with 4k+ English words.
With the rise of Large Language Models (LLMs) and their vision-enabled counterparts (VLMs), numerous works have investigated their capabilities in tasks that fuse the modalities of vision and language. In this work, we benchmark the extent to which VLMs are able to act as highly-trained phoneticians, interpreting spectrograms and waveforms of speech. To do this, we synthesise a novel dataset containing 4k+ English words spoken in isolation alongside stylistically consistent spectrogram and waveform figures. We test the ability of VLMs to understand these representations of speech through a multiple-choice task whereby models must predict the correct phonemic or graphemic transcription of a spoken word when presented amongst 3 distractor transcriptions that have been selected based on their phonemic edit distance to the ground truth. We observe that both zero-shot and finetuned models rarely perform above chance, demonstrating the requirement for specific parametric knowledge of how to interpret such figures, rather than paired samples alone.