111 citations · 391 across the 44 of their papers we have counts for
8 papers · 1 filter
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS
Katsuhito Sudoh, Takatomo Kano, Sashi Novitasari +3
This paper presents a newly developed, simultaneous neural speech-to-speech translation system and its evaluation. The system consists of three fully-incremental neural processing…
Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis
Sashi Novitasari, Andros Tjandra, Sakriani Sakti +1
Even though over seven hundred ethnic languages are spoken in Indonesia, the available technology remains limited that could support communication within indigenous communities as…
Sequence-to-Sequence Learning via Attention Transfer for Incremental Speech Recognition
Sashi Novitasari, Andros Tjandra, Sakriani Sakti +1
Attention-based sequence-to-sequence automatic speech recognition (ASR) requires a significant delay to recognize long utterances because the output is generated after receiving en…
Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time
Sashi Novitasari, Andros Tjandra, Tomoya Yanagita +2
Inspired by a human speech chain mechanism, a machine speech chain framework based on deep learning was recently proposed for the semi-supervised development of automatic speech re…
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework
Johanes Effendi, Andros Tjandra, Sakriani Sakti +1
Previous research has proposed a machine speech chain to enable automatic speech recognition (ASR) and text-to-speech synthesis (TTS) to assist each other in semi-supervised learni…
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
Ewan Dunbar, Julien Karadayi, Mathieu Bernard +6
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics f…
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
Fan Yang, Xin Chang, Chenyu Dang +4
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attri…
Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge
Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
In this paper, we report our submitted system for the ZeroSpeech 2020 challenge on Track 2019. The main theme in this challenge is to build a speech synthesizer without any textual…