  Many applications of speech technology require more and more audio data.
Automatic assessment of the quality of the collected recordings is important to
ensure they meet the requirements of the related applications. However,
effective and high performing assessment remains a challenging task without a
clean reference. In this paper, a novel model for audio quality assessment is
proposed by jointly using bidirectional long short-term memory and an attention
mechanism. The former is to mimic a human auditory perception ability to learn
information from a recording, and the latter is to further discriminate
interferences from desired signals by highlighting target related features. To
evaluate our proposed approach, the TIMIT dataset is used and augmented by
mixing with various natural sounds. In our experiments, two tasks are explored.
The first task is to predict an utterance quality score, and the second is to
identify where an anomalous distortion takes place in a recording. The obtained
results show that the use of our proposed approach outperforms a strong
baseline method and gains about 5% improvements after being measured by three
metrics, Linear Correlation Coefficient and Spearman Rank Correlation
Coefficient, and F1.
