LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION

no code implementations • 29 Sep 2021 • Liming Wang, Siyuan Feng, Mark A. Hasegawa-Johnson, Chang D. Yoo

Phonemes are defined by their relationship to words: changing a phoneme changes the word.

Representation Learning Self-Supervised Learning

The role of cue enhancement and frequency fine-tuning in hearing impaired phone recognition

no code implementations • 9 Aug 2019 • Ali Abavisani, Mark A. Hasegawa-Johnson

We studied the effects of frequency fine-tuning of the primary cue by presenting tokens of the same consonant but different vowels with similar SNR90.

Fast Generation for Convolutional Autoregressive Models

1 code implementation • 20 Apr 2017 • Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang

In this work, we describe a method to speed up generation in convolutional autoregressive models.

Fast Wavenet Generation Algorithm

6 code implementations • 29 Nov 2016 • Tom Le Paine, Pooya Khorrami, Shiyu Chang, Yang Zhang, Prajit Ramachandran, Mark A. Hasegawa-Johnson, Thomas S. Huang

This paper presents an efficient implementation of the Wavenet generation process called Fast Wavenet.

Streaming Recommender Systems

no code implementations • 21 Jul 2016 • Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.

Recommendation Systems