Search Results for author:
Found 9 papers, 5 papers with code
SSR7000: A Synchronized Corpus of Ultrasound Tongue Imaging for End-to-End Silent Speech Recognition
Although neural end-to-end models are successfully updating the state-of-the-art technology in the field of automatic speech recognition, SSR research based on ultrasound tongue imaging has still not evolved past cascaded DNN-HMM models due to the absence of a large dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data
Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth).
Duration-aware pause insertion using pre-trained language model for multi-speaker text-to-speech
We also leverage duration-aware pause insertion for more natural multi-speaker TTS.
Learning to Speak from Text: Zero-Shot Multilingual Text-to-Speech with Unsupervised Text Pretraining
While neural text-to-speech (TTS) has achieved human-like natural synthetic speech, multilingual TTS systems are limited to resource-rich languages due to the need for paired text and studio-quality audio data.
SpeechLMScore: Evaluating speech generation using speech language model
While human evaluation is the most reliable metric for evaluating speech generation systems, it is generally costly and time-consuming.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Empirical Study Incorporating Linguistic Knowledge on Filled Pauses for Personalized Spontaneous Speech Synthesis
We present a comprehensive empirical study for personalized spontaneous speech synthesis on the basis of linguistic knowledge.
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
Low-Latency Incremental Text-to-Speech Synthesis with Distilled Context Prediction Network
Although this method achieves comparable speech quality to that of a method that waits for the future context, it entails a huge amount of processing for sampling from the language model at each time step.
