Search Results for author:
Found 6 papers, 0 papers with code
Can We Train a Language Model Inside an End-to-End ASR Model? - Investigating Effective Implicit Language Modeling
The experiments show that we can enhance an ASR E2E model based on encoder-decoder architecture by pre-training the decoder with text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Do learned speech symbols follow Zipf's law?
In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf's law, akin to natural language symbols.
Preliminary Systematic Literature Review of Machine Learning System Development Process
Previous machine learning (ML) system development research suggests that emerging software quality attributes are a concern due to the probabilistic behavior of ML systems.
Wasserstein GAN and Waveform Loss-based Acoustic Model Training for Multi-speaker Text-to-Speech Synthesis Systems Using a WaveNet Vocoder
In order to reduce the mismatched characteristics between natural and generated acoustic features, we propose frameworks that incorporate either a conditional generative adversarial network (GAN) or its variant, Wasserstein GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that uses the WaveNet vocoder.
A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment
As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.
