Recent advancements in text-to-speech (TTS) powered by language models have
showcased remarkable capabilities in achieving naturalness and zero-shot voice
cloning. Notably, the decoder-only transformer is the prominent architecture in
this domain. However, transformers face challenges stemming from their
quadratic complexity in sequence length, impeding training on lengthy sequences
and resource-constrained hardware. Moreover they lack specific inductive bias
with regards to the monotonic nature of TTS alignments. In response, we propose
to replace transformers with emerging recurrent architectures and introduce
specialized cross-attention mechanisms for reducing repeating and skipping
issues. Consequently our architecture can be efficiently trained on long
samples and achieve state-of-the-art zero-shot voice cloning against baselines
of comparable size.

最近关于文本到语音合成（TTS）的研究表明，使用语言模型驱动的 TTS 展示了卓越的能力，能够实现自然度和零样本声音克隆。本文提出使用新兴递归架构替代变压器，并引入专门的交叉关注机制以减少重复和跳跃问题。结果，我们的架构能够在长音频样本上高效训练，并在相同规模的基准模型上实现最先进的零样本声音克隆。

Small-E：用线性注意力实现高效语音合成的小型语言模型

Small-E: Small Language Model with Linear Attention for Efficient Speech  Synthesis

Topic-controllable summarization is an emerging research area with a wide
range of potential applications. However, existing approaches suffer from
significant limitations. First, there is currently no established evaluation
metric for this task. Furthermore, existing methods built upon recurrent
architectures, which can significantly limit their performance compared to more
recent Transformer-based architectures, while they also require modifications
to the model's architecture for controlling the topic. In this work, we propose
a new topic-oriented evaluation measure to automatically evaluate the generated
summaries based on the topic affinity between the generated summary and the
desired topic. We also conducted a user study that validates the reliability of
this measure. Finally, we propose simple, yet powerful methods for
topic-controllable summarization either incorporating topic embeddings into the
model's architecture or employing control tokens to guide the summary
generation. Experimental results show that control tokens can achieve better
performance compared to more complicated embedding-based approaches while being
at the same time significantly faster.

该研究提出了一种新的主题导向的评估度量方法，用于自动评估生成的摘要与所需主题之间的关联度，并验证了其可靠性，同时提出了将主题嵌入到模型架构中或使用控制标记引导摘要生成的简单而强大的主题可控摘要方法，实验结果表明控制标记比更复杂的嵌入式方法表现更好，并且速度明显更快。

面向主题控制的摘要生成评估和 Transformer 方法

Topic-Aware Evaluation and Transformer Methods for Topic-Controllable Summarization

Memory-augmented neural networks consisting of a neural controller and an
external memory have shown potentials in long-term sequential learning. Current
RAM-like memory models maintain memory accessing every timesteps, thus they do
not effectively leverage the short-term memory held in the controller. We
hypothesize that this scheme of writing is suboptimal in memory utilization and
introduces redundant computation. To validate our hypothesis, we derive a
theoretical bound on the amount of information stored in a RAM-like system and
formulate an optimization problem that maximizes the bound. The proposed
solution dubbed Uniform Writing is proved to be optimal under the assumption of
equal timestep contributions. To relax this assumption, we introduce
modifications to the original solution, resulting in a solution termed Cached
Uniform Writing. This method aims to balance between maximizing memorization
and forgetting via overwriting mechanisms. Through an extensive set of
experiments, we empirically demonstrate the advantages of our solutions over
other recurrent architectures, claiming the state-of-the-arts in various
sequential modeling tasks.

本论文研究了使用外部存储器的记忆增强神经网络，提出了一种更优的存储方案，得到了对多种序列建模任务的最优解。