Search Results for author:
Found 15 papers, 6 papers with code
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction
In this paper, we propose a target speech extraction network that utilizes spatial information to enhance the performance of neural beamformer.
Exploring Representation Learning for Small-Footprint Keyword Spotting
To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training with pretrained model.
Relate auditory speech to EEG by shallow-deep attention-based network
Electroencephalography (EEG) plays a vital role in detecting how brain responses to different stimulus.
Improving Weakly Supervised Sound Event Detection with Causal Intervention
Existing weakly supervised sound event detection (WSSED) work has not explored both types of co-occurrences simultaneously, i. e., some sound events often co-occur, and their occurrences are usually accompanied by specific background sounds, so they would be inevitably entangled, causing misclassification and biased localization results with only clip-level supervision.
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding
We introduce phonology embedding to capture the English differences between different phonology.
Learning Decoupling Features Through Orthogonality Regularization
Bearing this in mind, a two-branch deep network (KWS branch and SV branch) with the same network structure is developed and a novel decoupling feature learning method is proposed to push up the performance of KWS and SV simultaneously where speaker-invariant keyword representations and keyword-invariant speaker representations are expected respectively.
PAMA-TTS: Progression-Aware Monotonic Attention for Stable Seq2Seq TTS With Accurate Phoneme Duration Control
Sequence expansion between encoder and decoder is a critical challenge in sequence-to-sequence TTS.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment
This paper introduces a new open-source speech corpus named "speechocean762" designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children.
Ranked #7 on
Phone-level pronunciation scoring
on speechocean762
AutoKWS: Keyword Spotting with Differentiable Architecture Search
Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption.
RawNet: Fast End-to-End Neural Vocoder
Neural network-based vocoders have recently demonstrated the powerful ability to synthesize high-quality speech.
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.
Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model
Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities.
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
Attention-Based End-to-End Speech Recognition on Voice Search
Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model.
