Speech has long been a barrier to effective communication and connection,
persisting as a challenge in our increasingly interconnected world. This
research paper introduces a transformative solution to this persistent obstacle
an end-to-end speech conversion framework tailored for Hindi-to-English
translation, culminating in the synthesis of English audio. By integrating
cutting-edge technologies such as XLSR Wav2Vec2 for automatic speech
recognition (ASR), mBART for neural machine translation (NMT), and a
Text-to-Speech (TTS) synthesis component, this framework offers a unified and
seamless approach to cross-lingual communication. We delve into the intricate
details of each component, elucidating their individual contributions and
exploring the synergies that enable a fluid transition from spoken Hindi to
synthesized English audio.

该研究论文介绍了一种针对印度 - 英语翻译的端到端语音转换框架，通过集成最新的技术实现自动语音识别、神经机器翻译和文本转语音合成组件，提供了一种统一无缝的跨语言交流方法。

使用 Bark、mBART 和经过微调的 XLSR Wav2Vec2 进行端到端印地语到英语语音转换

End to end Hindi to English speech conversion using Bark, mBART and a  finetuned XLSR Wav2Vec2

We present an unsupervised approach that converts the input speech of any
individual into audiovisual streams of potentially-infinitely many output
speakers. Our approach builds on simple autoencoders that project out-of-sample
data onto the distribution of the training set. We use Exemplar Autoencoders to
learn the voice, stylistic prosody, and visual appearance of a specific target
exemplar speech. In contrast to existing methods, the proposed approach can be
easily extended to an arbitrarily large number of speakers and styles using
only 3 minutes of target audio-video data, without requiring {\em any} training
data for the input speaker. To do so, we learn audiovisual bottleneck
representations that capture the structured linguistic content of speech. We
outperform prior approaches on both audio and video synthesis, and provide
extensive qualitative analysis on our project page --
this https URL

该论文提出了一种无监督方法，可以将任何个人的输入语音转换为无限数量的输出扬声器的音频可视化流，并使用先例自编码器学习特定目标样本语音的声音、风格韵律和视觉外观，它可以很容易地扩展到任意数量的扬声器和风格，而无需对输入扬声器进行任何训练数据。

基于样本自编码器的无监督音频视觉合成

Unsupervised Audiovisual Synthesis via Exemplar Autoencoders

We propose a nonparallel data-driven emotional speech conversion method. It
enables the transfer of emotion-related characteristics of a speech signal
while preserving the speaker's identity and linguistic content. Most existing
approaches require parallel data and time alignment, which is not available in
most real applications. We achieve nonparallel training based on an
unsupervised style transfer technique, which learns a translation model between
two distributions instead of a deterministic one-to-one mapping between paired
examples. The conversion model consists of an encoder and a decoder for each
emotion domain. We assume that the speech signal can be decomposed into an
emotion-invariant content code and an emotion-related style code in latent
space. Emotion conversion is performed by extracting and recombining the
content code of the source speech and the style code of the target emotion. We
tested our method on a nonparallel corpora with four emotions. Both subjective
and objective evaluations show the effectiveness of our approach.

本文提出了一种非并行数据驱动的情感语音转换方法，通过学习两个分布之间的翻译模型而实现非并行训练，该方法在音频内容编码与情感编码空间中进行情感转换，实现了保留说话人身份和语言特征的情感相关特征转移。