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DiffVoice: Text-to-Speech with Latent Diffusion 48

DiffVoice: Text-to-Speech with Latent Diffusion

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Zhijun Guo, Yiwei Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Subjective evaluations on LJSpeech and LibriTTS datasets demonstrate that our method beats the best publicly available systems in naturalness. By adopting recent generative inverse problem solving algorithms for diffusion models, DiffVoice achieves the state-of-the-art performance in text-based speech editing, and zero-shot adaptation. © 2023 IEEE.

关键词： diffusion probabilistic model speech editing speech synthesis variational autoencoder zero-shot adaptation

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Emodiff: Intensity Controllable Emotional Text-to-Speech with Soft-label Guidance 48

Emodiff: Intensity Controllable Emotional Text-to-Speech wit...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Guo, Yiwei Du, Chenpeng Chen, Xie Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emotion intensity can be manipulated by a proposed soft-label guidance technique derived from classifier guidance. Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and Neutral is set to α and 1 - α respectively. The α here represents the emotion intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can precisely control the emotion intensity while maintaining high voice quality. Moreover, diverse speech with specified emotion intensity can be generated by sampling in the reverse denoising process. © 2023 IEEE.

关键词： classifier guidance de-noising diffusion models emotion intensity control Emotional TTS

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Exploring Binary Classification Loss for Speaker Verification 48

Exploring Binary Classification Loss for Speaker Verificatio...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Han, Bing Chen, Zhengyang Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with degradation when evaluated on unseen speakers. In this work, we introduce SphereFace2 framework which uses several binary classifiers to train the speaker model in a pair-wise manner instead of performing multi-classification. Benefiting from this learning paradigm, it can efficiently alleviate the gap between training and evaluation. Experiments conducted on Voxceleb show that the SphereFace2 outperforms other existing loss functions, especially on hard trials. Besides, large margin fine-tuning strategy is proven to be compatible with it for further improvements. Finally, SphereFace2 also shows its strong robustness to class-wise noisy labels which has the potential to be applied in the semi-supervised training scenario with inaccurate estimated pseudo labels. © 2023 IEEE.

关键词： binary classification large margin fine-tuning speaker verification sphereface2

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Factorized AED: Factorized Attention-Based Encoder-Decoder for Text-Only Domain Adaptive ASR 48

Factorized AED: Factorized Attention-Based Encoder-Decoder f...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gong, Xun Wang, Wei Shao, Hang Chen, Xie Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

End-to-end automatic speech recognition (ASR) systems have gained popularity given their simplified architecture and promising results. However, text-only domain adaptation remains a big challenge for E2E systems. Text-to-speech (TTS) based approaches fine-tune ASR models by synthesized speech with an auxiliary TTS model, thus increase deployment costs. Language model (LM) fusion based approaches can achieve good performance but are sensitive to interpolation parameters. In order to factorize out the language component in the AED model, we propose the factorized attention-based encoder-decoder (Factorized AED) model whose decoder takes as input the posterior probabilities of a jointly trained LM. Moreover, in the context of domain adaptation, the domain specific LM serves as a plug-and-play component for a well-trained factorized AED model. In-domain experiments on LibriSpeech and out-of-domain experiments adapting from LibriSpeech to a variety of domains in GigaSpeech are conducted to validate the effectiveness of our proposed methods. Results show 20% / 24% relative word error rate (WER) reduction for LibriSpeech test sets and 8 ∼34% relative WER reduction for 8 GigaSpeech target domains test sets compared to the AED baseline. © 2023 IEEE.

关键词： domain adaptation end-to-end speech recognition factorized AED text-only

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HuBERT-AGG: Aggregated Representation Distillation of Hidden-Unit Bert for Robust Speech Recognition 48

HuBERT-AGG: Aggregated Representation Distillation of Hidden...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Wei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Self-supervised learning (SSL) has attracted widespread research interest since many successful SSL approaches such as wav2vec 2.0 and Hidden-unit BERT (HuBERT) have achieved promising results on speech-related tasks such as automatic speech recognition (ASR). However, few works have been conducted to improve the noise robustness of SSL models. In this paper, we propose HuBERT-AGG, a novel method that learns noise-invariant SSL representations for robust speech recognition by distilling aggregated layer-wise representations. Specifically, we learn an aggregator that computes the weighted sum of all hidden states of a pretrained vanilla Hu-BERT by fine-tuning it on a small portion of labeled data. Then a noise-robust HuBERT is trained on the simulated noisy speech by distilling from the aggregated representations and layer-wise hidden states produced by a pretrained vanilla HuBERT with parallel original speech as input. Experiments on libriSpeech simulated noisy test sets show 13.1%-17.0% relative word error rate (WER) reduction with very slight degradation on the original test sets. On CHiME-4 1-channel real speech test sets, we have surpassed the best results achieved by all published fully supervised ASR models as well as other SSL approaches adopting the same data usage as ours. © 2023 IEEE.

关键词： Distillation

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ECAPA++: Fine-grained Deep Embedding Learning for TDNN Based Speaker Verification 24

ECAPA++: Fine-grained Deep Embedding Learning for TDNN Based...

引用

24th International Speech Communication Association, Interspeech 2023

作者： Liu, Bei Qian, Yanmin MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we aim to bridge the performance gap between TDNN and 2D CNN based speaker verification systems. Specifically, three types of architectural enhancements to ECAPA-TDNN are proposed: 1) follow depth-first design to significantly increase network depth while maintaining its complexity. 2) introduce recursive convolution to better capture fine-grained speaker information. 3) propose pyramid-based multi-path feature enhancement module to yield more discriminative speaker representation. Experiments on Voxceleb show that our final model, named ECAPA++, achieves 25%, 23% and 24% relative improvements on Vox1-O, E and H respectively, while with 2.4x fewer parameters and 2.3x fewer FLOPs over the previous best TDNN-based system. Meanwhile, it is comparable to the state-of-the-art ResNet-based systems with higher computational efficiency. In addition, further performance gains can be achieved by fusing ECAPA++ and ResNet-based systems. © 2023 International Speech Communication Association. All rights reserved.

关键词： Computational efficiency

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LongFNT: Long-Form Speech Recognition with Factorized Neural Transducer 48

LongFNT: Long-Form Speech Recognition with Factorized Neural...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gong, Xun Wu, Yu Li, Jinyu Liu, Shujie Zhao, Rui Chen, Xie Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China Microsoft

ISBN: (纸本)9781728163277

Traditional automatic speech recognition (ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is more practical in real scenarios. Simply attending longer transcription history for a vanilla neural transducer model shows no much gain in our preliminary experiments, since the prediction network is not a pure language model. This motivates us to leverage the factorized neural transducer structure, containing a real language model, the vocabulary predictor. We propose the LongFNT-Text architecture, which fuses the sentence-level long-form features directly with the output of the vocabulary predictor and then embeds token-level long-form features inside the vocabulary predictor, with a pre-trained contextual encoder RoBERTa to further boost the performance. Moreover, we propose the LongFNT architecture by extending the long-form speech to the original speech input and achieve the best performance. The effectiveness of our LongFNT approach is validated on LibriSpeech and GigaSpeech corpora with 19% and 12% relative word error rate (WER) reduction, respectively. © 2023 IEEE.

关键词： Speech recognition

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Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Challenge 48

Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Ch...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Du, Chenpeng Guo, Yiwei Shen, Feiyu Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

In this paper, we describe the systems developed by the SJTU X-LANCE team for LIMMITS 2023 Challenge, and we mainly focus on the winning system on naturalness for track 1. The aim of this challenge is to build a multi-speaker multi-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Each of the languages has a male and a female speaker in the given dataset. In track 1, only 5 hours data from each speaker can be selected to train the TTS model. Our system is based on the recently proposed VQTTS that utilizes VQ acoustic feature rather than mel-spectrogram. We introduce additional speaker embeddings and language embeddings to VQTTS for controlling the speaker and language information. In the cross-lingual evaluations where we need to synthesize speech in a cross-lingual speaker's voice, we provide a native speaker's embedding to the acoustic model and the target speaker's embedding to the vocoder. In the subjective MOS listening test on naturalness, our system achieves 4.77 which ranks first. © 2023 IEEE.

关键词： Embeddings

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CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions

CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Zhang, Hanchong Cao, Ruisheng Xu, Hongshen Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9798891761148

Recently, Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to-SQL task and attempt to enhance the LLMs’ reasoning capacity when generating SQL queries. In the conversational context, the current SQL query can be modified from the preceding SQL query with only a few operations due to the context dependency. We introduce our method called CoE-SQL1 which can prompt LLMs to generate the SQL query based on the previously generated SQL query with an edition chain. We also conduct extensive ablation studies to determine the optimal configuration of our approach. Our approach outperforms different in-context learning baselines stably and achieves state-of-the-art performances on two benchmarks SParC and CoSQL using LLMs, which is also competitive to the SOTA fine-tuned models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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UP2ME: univariate pre-training to multivariate fine-tuning as a general-purpose framework for multivariate time series analysis 24

UP2ME: univariate pre-training to multivariate fine-tuning a...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yunhao Zhang Minghao Liu Shengyang Zhou Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong UniversitySchool of Artificial Intelligence & Department of Computer Science and Engineering & MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University

Despite the success of self-supervised pretraining in texts and images, applying it to multivariate time series (MTS) falls behind tailored methods for tasks like forecasting, imputation and anomaly detection. We propose a general-purpose framework, named UP2ME (Univariate Pretraining to Multivariate Fine-tuning). It conducts task-agnostic pre-training when downstream tasks are unspecified. Once the task and setting (e.g. forecasting length) are determined, it gives sensible solutions with frozen pre-trained parameters, which has not been achieved before. UP2ME is further refined by fine-tuning. A univariate-to-multivariate paradigm is devised to address the heterogeneity of temporal and cross-channel dependencies. In univariate pre-training, univariate instances with diverse lengths are generated for Masked AutoEncoder (MAE) pre-training, discarding cross-channel dependency. The pretrained model handles downstream tasks by formulating them into specific mask-reconstruction problems. In multivariate fine-tuning, it constructs a dependency graph among channels using the pre-trained encoder to enhance cross-channel dependency capture. Experiments on eight real-world datasets show its SOTA performance in forecasting and imputation, approaching task-specific performance in anomaly detection. Our code is available at https://***/Thinklab-SJTU/UP2ME.

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