Forced alignment systems automatically determine boundaries between segments
in speech data, given an orthographic transcription. These tools are
commonplace in phonetics to facilitate the use of speech data that would be
infeasible to manually transcribe and segment. In the present paper, we
describe a new neural network-based forced alignment system, the Mason-Alberta
Phonetic Segmenter (MAPS). The MAPS aligner serves as a testbed for two
possible improvements we pursue for forced alignment systems. The first is
treating the acoustic model in a forced aligner as a tagging task, rather than
a classification task, motivated by the common understanding that segments in
speech are not truly discrete and commonly overlap. The second is an
interpolation technique to allow boundaries more precise than the common 10 ms
limit in modern forced alignment systems. We compare configurations of our
system to a state-of-the-art system, the Montreal Forced Aligner. The tagging
approach did not generally yield improved results over the Montreal Forced
Aligner. However, a system with the interpolation technique had a 27.92%
increase relative to the Montreal Forced Aligner in the amount of boundaries
within 10 ms of the target on the test set. We also reflect on the task and
training process for acoustic modeling in forced alignment, highlighting how
the output targets for these models do not match phoneticians' conception of
similarity between phones and that reconciliation of this tension may require
rethinking the task and output targets or how speech itself should be
segmented.

在本研究中，我们描述了一种新的基于神经网络的强制韵律对齐系统，Mason-Alberta 声学分割器（MAPS），它作为测试平台用于改进强制韵律对齐系统的两种潜在方法：将强制对齐器中的声学模型视为标记任务而非分类任务，以及使用一种插值技术让边界更精确。与现有的最先进系统 Montreal Forced Aligner 相比，标记方法通常并没有取得更好的结果，但是使用插值技术的系统在测试集中比 Montreal Forced Aligner 的目标边界多 10 毫秒的数量上增加了 27.92%。我们还思考了强制对齐中声学建模的任务和训练过程，并强调模型的输出目标与语音学家对音素间相似度的理解不完全相符，解决这种紧张可能需要重新思考任务和输出目标，或者如何对语音进行分割。

Mason-Alberta 语音划分器：基于深度神经网络和插值的强制对齐系统

The Mason-Alberta Phonetic Segmenter: A forced alignment system based on  deep neural networks and interpolation

This paper proposes an effective emotion control method for an end-to-end
text-to-speech (TTS) system. To flexibly control the distinct characteristic of
a target emotion category, it is essential to determine embedding vectors
representing the TTS input. We introduce an inter-to-intra emotional distance
ratio algorithm to the embedding vectors that can minimize the distance to the
target emotion category while maximizing its distance to the other emotion
categories. To further enhance the expressiveness of a target speech, we also
introduce an effective interpolation technique that enables the intensity of a
target emotion to be gradually changed to that of neutral speech. Subjective
evaluation results in terms of emotional expressiveness and controllability
show the superiority of the proposed algorithm to the conventional methods.

本论文提出了一种有效的情感控制方法，用于端到端的文本到语音（TTS）系统，使用嵌入向量表示 TTS 输入并结合了内外情感距离比算法和插值技术来提高发音的表现力和可控性。主观评估表明所提算法优于传统方法。