Search Results for author: Nanxin Chen

Found 22 papers, 7 papers with code

How to Estimate Model Transferability of Pre-Trained Speech Models?

no code implementations1 Jun 2023 Zih-Ching Chen, Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Shou-Yiin Chang, Rohit Prabhavalkar, Hung-Yi Lee, Tara N. Sainath

In this work, we introduce a ``score-based assessment'' framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks.

From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition

no code implementations19 Jan 2023 Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Rohit Prabhavalkar, Tara N. Sainath, Trevor Strohman

In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-trained English automatic speech recognition (ASR) models to recognize the other languages.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition

no code implementations2 Nov 2022 Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, Chin-Hui Lee

We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios.

Spoken Command Recognition

Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation

no code implementations28 Oct 2022 Nobuyuki Morioka, Heiga Zen, Nanxin Chen, Yu Zhang, Yifan Ding

Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model.

Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR

no code implementations18 Oct 2022 Zhehuai Chen, Ankur Bapna, Andrew Rosenberg, Yu Zhang, Bhuvana Ramabhadran, Pedro Moreno, Nanxin Chen

First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.

Representation Learning speech-recognition +2

SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping

no code implementations31 Mar 2022 Yuma Koizumi, Heiga Zen, Kohei Yatabe, Nanxin Chen, Michiel Bacchiani

Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features.

Denoising Speech Enhancement

A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation

no code implementations11 Oct 2021 Yosuke Higuchi, Nanxin Chen, Yuya Fujita, Hirofumi Inaguma, Tatsuya Komatsu, Jaesong Lee, Jumon Nozaki, Tianzi Wang, Shinji Watanabe

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to autoregressive baselines.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

2 code implementations17 Jun 2021 Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, Najim Dehak, William Chan

The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform.

Speech Synthesis Text-To-Speech Synthesis

Focus on the present: a regularization method for the ASR source-target attention layer

no code implementations2 Nov 2020 Nanxin Chen, Piotr Żelasko, Jesús Villalba, Najim Dehak

This paper introduces a novel method to diagnose the source-target attention in state-of-the-art end-to-end speech recognition models with joint connectionist temporal classification (CTC) and attention training.

speech-recognition Speech Recognition

WaveGrad: Estimating Gradients for Waveform Generation

6 code implementations ICLR 2021 Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, William Chan

This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.

Speech Synthesis Text-To-Speech Synthesis

Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict

no code implementations18 May 2020 Yosuke Higuchi, Shinji Watanabe, Nanxin Chen, Tetsuji Ogawa, Tetsunori Kobayashi

In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.

Audio and Speech Processing Sound

Improving Language Identification for Multilingual Speakers

no code implementations29 Jan 2020 Andrew Titus, Jan Silovsky, Nanxin Chen, Roger Hsiao, Mary Young, Arnab Ghoshal

Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language.

Language Identification Spoken language identification

Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

3 code implementations23 Oct 2019 Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, Junichi Yamagishi

While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers.

Audio and Speech Processing

ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

1 code implementation1 Apr 2019 Cheng-I Lai, Nanxin Chen, Jesús Villalba, Najim Dehak

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT).

Feature Engineering Voice Conversion

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