Efficient Speech Quality Assessment Using Self-supervised Framewise Embeddings
2022 · Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow, et al.
Abstract
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers. The current state-of-the-art systems are based on framewise speech features (hand-engineered or learnable) combined with time dependency modeling. This paper proposes an efficient system with results comparable to the best performing model in the ConferencingSpeech 2022 challenge. Our proposed system is characterized by a smaller number of parameters (40-60x), fewer FLOPS (100x), lower memory consumption (10-15x), and lower latency (30x). Speech quality practitioners can therefore iterate much faster, deploy the system on resource-limited hardware, and, overall, the proposed system contributes to sustainable machine learning. The paper also concludes that framewise embeddings outperform utterance-level embeddings and that multi-task training with acoustic conditions modeling does not degrade speech quality prediction while providing better
Authors
(none)
Tags
Stats
Related papers
- A Pre-training Framework That Encodes Noise Information For Speech Quality Assessment (2024)3.58
- The Efficacy Of Self-supervised Speech Models For Audio Representations (2022)0.00
- Pre-trained Speech Representations As Feature Extractors For Speech Quality Assessment In Online Conferencing Applications (2022)5.84
- End-to-end And Self-supervised Learning For Compare 2022 Stuttering Sub-challenge (2022)2.26
- Robust Vocal Quality Feature Embeddings For Dysphonic Voice Detection (2022)7.16
- Efficient Infusion Of Self-supervised Representations In Automatic Speech Recognition (2024)0.00
- A Comparison Of Self-supervised Speech Representations As Input Features For Unsupervised Acoustic Word Embeddings (2020)7.16
- Fine-tuning Strategies For Faster Inference Using Speech Self-supervised Models: A Comparative Study (2023)8.35