Thu-3-2-4 Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models

Abstract: 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 based on combining bidirectional long short-term memory with an attention mechanism. The former is to mimic a human listening 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 targets are explored. The first target is to predict utterance quality score, and the second one is to identify where an anomaly distortion takes place in a recording. The obtained results show that the use of our proposed approach outperforms a strong baseline method using only BLSTM and gains about 5% improvements after being measured by three metrics, Linear Correlation Coefficient and Spearman’s Rank Correlation Coefficient, and F1.