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。