Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition

By Krishna C. Puvvada, Nithin Rao Koluguri, Kunal Dhawan, Jagadeesh Balam, Boris Ginsburg. 2023

30 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

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BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data (2024, background): “Anyone who feels that if so many more students whom we haven’t actually admitted are sitting in on the course than ones we have that the room had to be changed, then probably auditors will have to be excluded, is likely to agree that the curriculum needs revision.”