  Discrete audio representation, aka audio tokenization, has seen renewed
interest driven by its potential to facilitate the application of text language
modeling approaches in audio domain. To this end, various compression and
representation-learning based tokenization schemes have been proposed. However,
there is limited investigation into the performance of compression-based audio
tokens compared to well-established mel-spectrogram features across various
speaker and speech related tasks. In this paper, we evaluate compression based
audio tokens on three tasks: Speaker Verification, Diarization and
(Multi-lingual) Speech Recognition. Our findings indicate that (i) the models
trained on audio tokens perform competitively, on average within $1\%$ of
mel-spectrogram features for all the tasks considered, and do not surpass them
yet. (ii) these models exhibit robustness for out-of-domain narrowband data,
particularly in speaker tasks. (iii) audio tokens allow for compression to 20x
compared to mel-spectrogram features with minimal loss of performance in speech
and speaker related tasks, which is crucial for low bit-rate applications, and
(iv) the examined Residual Vector Quantization (RVQ) based audio tokenizer
exhibits a low-pass frequency response characteristic, offering a plausible
explanation for the observed results, and providing insight for future
tokenizer designs.
