ロボット自己音声フィルタリング後の半リアルタイム音声強調：スペクトル過減算の補正
Spectral oversubtraction? An approach for speech enhancement after robot ego speech filtering in semi-real-time
ロボットが発話中に単一マイクで人間の割り込み音声を検出する際、スペクトル減算の過減算を二重マスクCMGANで補正し、半リアルタイム処理を可能にする手法を提案した。
著者: Yue Li, Koen V. Hindriks, Florian A. Kunneman
分類: cs.RO, cs.SD, eess.AS
原文アブストラクト
Spectral subtraction, widely used for its simplicity, has been employed to address the Robot Ego Speech Filtering (RESF) problem for detecting speech contents of human interruption from robot's single-channel microphone recordings when it is speaking. However, this approach suffers from oversubtraction in the fundamental frequency range (FFR), leading to degraded speech content recognition. To address this, we propose a Two-Mask Conformer-based Metric Generative Adversarial Network (CMGAN) to enhance the detected speech and improve recognition results. Our model compensates for oversubtracted FFR values with high-frequency information and long-term features and then de-noises the new spectrogram. In addition, we introduce an incremental processing method that allows semi-real-time audio processing with streaming input on a network trained on long fixed-length input. Evaluations of two datasets, including one with unseen noise, demonstrate significant improvements in recognition accuracy and the effectiveness of the proposed two-mask approach and incremental processing, enhancing the robustness of the proposed RESF pipeline in real-world HRI scenarios.
