Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering
Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-frequency filtering (ZFF) without making any explicit model assumptions about the speech signal. This paper investigates the potential of zero-frequency filtering for jointly modeling voice source and vocal tract system information, and proposes two approaches for VAD. The first approach demarcates voiced regions using a composite signal composed of different zero-frequency filtered signals. The second approach feeds the composite signal as input to the rVAD algorithm. These approaches are compared with other supervised and unsupervised VAD methods in the literature, and are evaluated on the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show that the proposed ZFF-based methods perform comparable to state-of-art VAD methods and are more invariant to added degradation and different channel characteristics.
Code (1)
Tasks
Action DetectionActivity DetectionSimilar Papers 제목 키워드 기반
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
This paper presents an unsupervised segment-based method for robust voice activity detection (rVAD). The method consists of two passes of denoising followed by a voice activity detection (VAD) stage. In the first pass, h…
Action DetectionActivity DetectionDenoisingSpeaker Verification+1Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction
Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection…
Action DetectionActivity Detectionspeaker-diarizationSpeaker DiarizationAn End-to-End Architecture for Keyword Spotting and Voice Activity Detection
We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Conn…
Action DetectionActivity DetectionGeneral ClassificationKeyword SpottingVoice Activity Projection: Self-supervised Learning of Turn-taking Events
The modeling of turn-taking in dialog can be viewed as the modeling of the dynamics of voice activity of the interlocutors. We extend prior work and define the predictive task of Voice Activity Projection, a general, sel…
Self-Supervised LearningA Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection
This paper presents a smartphone app that performs real-time voice activity detection based on convolutional neural network. Real-time implementation issues are discussed showing how the slow inference time associated wi…
Action DetectionActivity DetectionAudio Signal RecognitionNoise Estimation