MSR-HuBERT: Self-supervised Pre-training for Adaptation to Multiple Sampling Rates
Abstract
Self-supervised learning (SSL) has advanced speech processing. However, existing speech SSL methods typically assume a single sampling rate and struggle with mixed-rate data due to temporal resolution mismatch. To address this limitation, we propose MSRHuBERT, a multi-sampling-rate adaptive pre-training method. Building on HuBERT, we replace its single-rate downsampling CNN with a multi-sampling-rate adaptive downsampling CNN that maps raw waveforms from different sampling rates to a shared temporal resolution without resampling. This design enables unified mixed-rate pre-training and fine-tuning. In experiments spanning 16 to 48 kHz, MSRHuBERT outperforms HuBERT on speech recognition and full-band speech reconstruction, preserving high-frequency detail while modeling low-frequency semantic structure. Moreover, MSRHuBERT retains HuBERT's mask-prediction objective and Transformer encoder, so existing analyses and improvements that were developed for HuBERT can apply directly.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents MSRHuBERT, a self-supervised learning framework that effectively addresses the resolution mismatch problem in speech processing across multiple sampling rates. The technical contributions are substantial, with a well-justified methodology and promising experimental results, positioning this work as a notable advancement in the field of audio machine learning.
Methodology
The proposed MSRHuBERT method introduces a novel multi-sampling-rate adaptive downsampling CNN that effectively addresses the resolution mismatch problem in self-supervised speech learning. By allowing the model to process audio at various sampling rates without resampling, it preserves high-frequency information critical for tasks like speech reconstruction while maintaining low-frequency semantic content for ASR. The methodology is well-structured, retaining the core elements of the HuBERT framework, which facilitates the integration of existing improvements and analyses. The approach is theoretically sound and presents a clear advancement over existing methods.
Results & evaluation
The experiments conducted span multiple sampling rates (16 kHz to 48 kHz) and evaluate both ASR and full-band speech reconstruction tasks. The results demonstrate that MSRHuBERT outperforms the baseline HuBERT model across various metrics, showcasing its effectiveness in preserving high-frequency details while maintaining low-frequency content. The use of diverse datasets and the systematic evaluation of performance across different sampling rates strengthens the findings. However, the paper could benefit from additional comparative analyses with other state-of-the-art models beyond HuBERT.
Reproducibility
The paper provides a detailed description of the experimental setup, including the datasets used and the training configurations. However, the absence of a publicly available code repository or demo URL limits reproducibility. Future work should consider releasing the model and code to facilitate validation by the research community.
Limitations
One limitation is the reliance on the HuBERT architecture, which may restrict the generalizability of the proposed method to other architectures. Additionally, while the paper addresses the resolution mismatch problem, it does not explore the implications of using the model in real-world applications where sampling rates may vary dynamically. The paper could also expand on potential computational costs associated with the multi-sampling-rate adaptive downsampling CNN.
Broader impact
The implications of this research are significant for the field of speech processing, particularly in applications requiring robust performance across varying audio qualities and sampling rates. The ability to handle mixed-rate data without loss of information can enhance the usability of speech models in diverse environments, potentially leading to improvements in voice recognition systems, virtual assistants, and other audio applications. This paper presents MSRHuBERT, a self-supervised learning framework that effectively addresses the resolution mismatch problem in speech processing across multiple sampling rates. The technical contributions are substantial, with a well-justified methodology and promising experimental results, positioning this work as a notable advancement in the field of audio machine learning.
Author of this paper? Send a correction.