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Found 8 papers, 0 papers with code
Bridging the Gap between Audio and Text using Parallel-attention for User-defined Keyword Spotting
This paper proposes a novel user-defined keyword spotting framework that accurately detects audio keywords based on text enrollment.
Faces that Speak: Jointly Synthesising Talking Face and Speech from Text
The goal of this work is to simultaneously generate natural talking faces and speech outputs from text.
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers.
Ranked #1 on
Speech Separation
on WSJ0-5mix
Neural Speech Enhancement with Very Low Algorithmic Latency and Complexity via Integrated Full- and Sub-Band Modeling
We propose FSB-LSTM, a novel long short-term memory (LSTM) based architecture that integrates full- and sub-band (FSB) modeling, for single- and multi-channel speech enhancement in the short-time Fourier transform (STFT) domain.
That's What I Said: Fully-Controllable Talking Face Generation
The goal of this paper is to synthesise talking faces with controllable facial motions.
Joint unsupervised and supervised learning for context-aware language identification
Language identification (LID) recognizes the language of a spoken utterance automatically.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
CrossSpeech: Speaker-independent Acoustic Representation for Cross-lingual Speech Synthesis
In this paper, we propose CrossSpeech which improves the quality of cross-lingual speech by effectively disentangling speaker and language information in the level of acoustic feature space.
Metric Learning for User-defined Keyword Spotting
In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics.
