Non-Intrusive Air Traffic Control Speech Quality Assessment with ResNet-BiLSTM

Abstract

In the current field of air traffic control speech, there is a lack of effective objective speech quality evaluation methods. This paper proposes a new network framework based on ResNet–BiLSTM to address this issue. Firstly, the mel-spectrogram of the speech signal is segmented using the sliding window technique. Next, a preceding feature extractor composed of convolutional and pooling layers is employed to extract shallow features from the mel-spectrogram segment. Then, ResNet is utilized to extract spatial features from the shallow features, while BiLSTM is used to extract temporal features, and these features are horizontally concatenated. Finally, based on the concatenated spatiotemporal features, the final speech quality score is computed using fully connected layers. We conduct experiments on the air traffic control speech database and compare the objective scoring results with the subjective scoring results. The experimental results demonstrate that the proposed method has a high correlation with the mean opinion score (MOS) of air traffic control speech.

Improvement of Multimodal Emotion Recognition Based on Temporal-Aware Bi-Direction Multi-Scale Network and Multi-Head Attention Mechanisms

“…With the advancement of deep learning, researchers have turned to deep learning methods for extracting speech emotion features. Common deep learning methods for speech feature extraction include Convolutional Neural Networks (CNNs) [38,39], Recurrent Neural Networks (RNNs) [40], Bidirectional Long Short-Term Memory (BiLSTM) [41][42][43], etc. The Wav2vec2.0 version used in this paper is an end-to-end training approach that can learn representative feature descriptions directly from audio data through self-supervised learning, eliminating the need for manual parameter tuning.…”