Single-Channel Speech Separation with Auxiliary Speaker Embeddings
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers. The proposed model is a neural network based on residual blocks, and uses learnt speaker embeddings created from additional clean context recordings of the two speakers as input to assist in attributing the different time-frequency bins to the two speakers. In experiments, we show that the proposed model yields good performance in the source separation task, and outperforms the state-of-the-art baselines. Specifically, separating speech from the challenging VoxCeleb dataset, the proposed model yields 4.79dB signal-to-distortion ratio, 8.44dB signal-to-artifacts ratio and 7.11dB signal-to-interference ratio.
Code (0)
등록된 구현이 없습니다.
Tasks
Speech SeparationSimilar Papers 제목 키워드 기반
FaceFilter: Audio-visual speech separation using still images
The objective of this paper is to separate a target speaker's speech from a mixture of two speakers using a deep audio-visual speech separation network. Unlike previous works that used lip movement on video clips or pre-…
Speech SeparationNew Insights on Target Speaker Extraction
Speaker extraction (SE) aims to segregate the speech of a target speaker from a mixture of interfering speakers with the help of auxiliary information. Several forms of auxiliary information have been employed in single-…
Speaker SeparationTarget Speaker ExtractionMany-Speakers Single Channel Speech Separation with Optimal Permutation Training
Single channel speech separation has experienced great progress in the last few years. However, training neural speech separation for a large number of speakers (e.g., more than 10 speakers) is out of reach for the curre…
Speech SeparationNeural Blind Source Separation and Diarization for Distant Speech Recognition
This paper presents a neural method for distant speech recognition (DSR) that jointly separates and diarizes speech mixtures without supervision by isolated signals. A standard separation method for multi-talker DSR is a…
blind source separationDistant Speech Recognitionspeaker-diarizationSpeaker Diarization+2Joint Sound Source Separation and Speaker Recognition
Non-negative Matrix Factorization (NMF) has already been applied to learn speaker characterizations from single or non-simultaneous speech for speaker recognition applications. It is also known for its good performance i…
blind source separationSpeaker Recognition