In recent years, automatic speech recognition (ASR) systems have
significantly improved, especially in languages with a vast amount of
transcribed speech data. However, ASR systems tend to perform poorly for
low-resource languages with fewer resources, such as minority and regional
languages. This study introduces a novel pipeline designed to generate ASR
training datasets from audiobooks, which typically feature a single transcript
associated with hours-long audios. The common structure of these audiobooks
poses a unique challenge due to the extensive length of audio segments, whereas
optimal ASR training requires segments ranging from 4 to 15 seconds. To address
this, we propose a method for effectively aligning audio with its corresponding
text and segmenting it into lengths suitable for ASR training. Our approach
simplifies data preparation for ASR systems in low-resource languages and
demonstrates its application through a case study involving the Armenian
language. Our method, which is "portable" to many low-resource languages, not
only mitigates the issue of data scarcity but also enhances the performance of
ASR models for underrepresented languages.

本研究介绍了一种用于从有声读物生成 ASR 训练数据集的新型流程，以应对资源稀缺语言中自动语音识别系统性能较差的问题。该方法通过有效地对齐音频和相应的文本，并将其分割成适合 ASR 训练的长度，简化了资源稀缺语言中 ASR 系统的数据准备工作，并通过对亚美尼亚语的案例研究证明了其应用价值。这种方法可以适用于许多资源稀缺语言，不仅解决了数据匮乏问题，还提高了低资源语言的 ASR 模型性能。

启用低资源语言的 ASR：一个全面的数据集创建方法

Enabling ASR for Low-Resource Languages: A Comprehensive Dataset  Creation Approach

In the ever-evolving digital audio landscape, Spotify, well-known for its
music and talk content, has recently introduced audiobooks to its vast user
base. While promising, this move presents significant challenges for
personalized recommendations. Unlike music and podcasts, audiobooks, initially
available for a fee, cannot be easily skimmed before purchase, posing higher
stakes for the relevance of recommendations. Furthermore, introducing a new
content type into an existing platform confronts extreme data sparsity, as most
users are unfamiliar with this new content type. Lastly, recommending content
to millions of users requires the model to react fast and be scalable. To
address these challenges, we leverage podcast and music user preferences and
introduce 2T-HGNN, a scalable recommendation system comprising Heterogeneous
Graph Neural Networks (HGNNs) and a Two Tower (2T) model. This novel approach
uncovers nuanced item relationships while ensuring low latency and complexity.
We decouple users from the HGNN graph and propose an innovative multi-link
neighbor sampler. These choices, together with the 2T component, significantly
reduce the complexity of the HGNN model. Empirical evaluations involving
millions of users show significant improvement in the quality of personalized
recommendations, resulting in a +46% increase in new audiobooks start rate and
a +23% boost in streaming rates. Intriguingly, our model's impact extends
beyond audiobooks, benefiting established products like podcasts.

通过基于异构图神经网络的可扩展推荐系统 2T-HGNN，我们利用 Spotify 的用户偏好，解决了将有声读物推荐给用户所面临的挑战，提高了个性化推荐的质量，并且产生了积极的影响。

通过图神经网络个性化推荐 Spotify 音频书

Personalized Audiobook Recommendations at Spotify Through Graph Neural  Networks

Cross-speaker style transfer aims to extract the speech style of the given
reference speech, which can be reproduced in the timbre of arbitrary target
speakers. Existing methods on this topic have explored utilizing
utterance-level style labels to perform style transfer via either global or
local scale style representations. However, audiobook datasets are typically
characterized by both the local prosody and global genre, and are rarely
accompanied by utterance-level style labels. Thus, properly transferring the
reading style across different speakers remains a challenging task. This paper
aims to introduce a chunk-wise multi-scale cross-speaker style model to capture
both the global genre and the local prosody in audiobook speeches. Moreover, by
disentangling speaker timbre and style with the proposed switchable adversarial
classifiers, the extracted reading style is made adaptable to the timbre of
different speakers. Experiment results confirm that the model manages to
transfer a given reading style to new target speakers. With the support of
local prosody and global genre type predictor, the potentiality of the proposed
method in multi-speaker audiobook generation is further revealed.

通过提出的分块多尺度跨说话人风格模型以及可切换式对抗分类器，本文旨在解决跨说话人的阅读风格转移问题，并证实其在多人语音书籍生成中的潜力。