Automatic speech quality assessment has raised more attention as an
alternative or support to traditional perceptual clinical evaluation. However,
most research so far only gains good results on simple tasks such as binary
classification, largely due to data scarcity. To deal with this challenge,
current works tend to segment patients' audio files into many samples to
augment the datasets. Nevertheless, this approach has limitations, as it
indirectly relates overall audio scores to individual segments. This paper
introduces a novel approach where the system learns at the audio level instead
of segments despite data scarcity. This paper proposes to use the pre-trained
Wav2Vec2 architecture for both SSL, and ASR as feature extractor in speech
assessment. Carried out on the HNC dataset, our ASR-driven approach established
a new baseline compared with other approaches, obtaining average $MSE=0.73$ and
$MSE=1.15$ for the prediction of intelligibility and severity scores
respectively, using only 95 training samples. It shows that the ASR based
Wav2Vec2 model brings the best results and may indicate a strong correlation
between ASR and speech quality assessment. We also measure its ability on
variable segment durations and speech content, exploring factors influencing
its decision.

自动语音质量评估中，由于数据稀缺，大多数研究仅在二元分类等简单任务上取得良好结果。本文提出了一种新的方法，通过采用预训练的 Wav2Vec2 架构作为语音评估中的特征提取器，将学习系统从片段级别提升至音频级别，从而建立了一个新的基准，使得只使用 95 个训练样本可以实现对可懂度和严重程度得分的预测，平均均方误差分别为 0.73 和 1.15。结果表明，基于 ASR 的 Wav2Vec2 模型带来了最佳结果，并且可能暗示了 ASR 与语音质量评估之间的强相关性。同时，我们还评估了该方法在变长片段持续时间和语音内容等因素上的影响。

在数据稀缺环境中利用 ASR 驱动的 Wav2Vec2 探索病态语音质量评估

Exploring Pathological Speech Quality Assessment with ASR-Powered  Wav2Vec2 in Data-Scarce Context

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 interpretation.

本文提出了一种以帧特征为基础，采用时间依赖建模的自动语音质量评估系统，相较之下与其它表现最佳的模型相比，该系统参数更少（约 40-60 倍）、计算量更少（约 100 倍）、内存消耗更少（约 10-15 倍）且延迟更低（约 30 倍），并且发现帧嵌入优于语句级嵌入，而多任务训练时进行声学条件建模不会降低语音质量预测能力，同时提供更好的解释。