Segmentation for continuous Automatic Speech Recognition (ASR) has
traditionally used silence timeouts or voice activity detectors (VADs), which
are both limited to acoustic features. This segmentation is often overly
aggressive, given that people naturally pause to think as they speak.
Consequently, segmentation happens mid-sentence, hindering both punctuation and
downstream tasks like machine translation for which high-quality segmentation
is critical. Model-based segmentation methods that leverage acoustic features
are powerful, but without an understanding of the language itself, these
approaches are limited. We present a hybrid approach that leverages both
acoustic and language information to improve segmentation. Furthermore, we show
that including one word as a look-ahead boosts segmentation quality. On
average, our models improve segmentation-F0.5 score by 9.8% over baseline. We
show that this approach works for multiple languages. For the downstream task
of machine translation, it improves the translation BLEU score by an average of
1.05 points.

研究利用混合声学和语言信息的方法改善了自动语音识别中过分依赖声学特征而出现的分割问题，在基准测试中平均提高了 9.8％的分割 - F0.5 分数，该方法适用于多种语言，可显著提高机器翻译 BLEU 得分约 1.05 个点。

利用声学语言特征和先行技术进行智能语音分段

Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead

Statistical shape models (SSMs) represent a class of shapes as a normal
distribution of point variations, whose parameters are estimated from example
shapes. Principal component analysis (PCA) is applied to obtain a
low-dimensional representation of the shape variation in terms of the leading
principal components. In this paper, we propose a generalization of SSMs,
called Gaussian Process Morphable Models (GPMMs). We model the shape variations
with a Gaussian process, which we represent using the leading components of its
Karhunen-Loeve expansion. To compute the expansion, we make use of an
approximation scheme based on the Nystrom method. The resulting model can be
seen as a continuous analogon of an SSM. However, while for SSMs the shape
variation is restricted to the span of the example data, with GPMMs we can
define the shape variation using any Gaussian process. For example, we can
build shape models that correspond to classical spline models, and thus do not
require any example data. Furthermore, Gaussian processes make it possible to
combine different models. For example, an SSM can be extended with a spline
model, to obtain a model that incorporates learned shape characteristics, but
is flexible enough to explain shapes that cannot be represented by the SSM. We
introduce a simple algorithm for fitting a GPMM to a surface or image. This
results in a non-rigid registration approach, whose regularization properties
are defined by a GPMM. We show how we can obtain different registration
schemes,including methods for multi-scale, spatially-varying or hybrid
registration, by constructing an appropriate GPMM. As our approach strictly
separates modelling from the fitting process, this is all achieved without
changes to the fitting algorithm. We show the applicability and versatility of
GPMMs on a clinical use case, where the goal is the model-based segmentation of
3D forearm images.

本文提出了一种新的广义形状模型 Gaussian Process Morphable Models (GPMMs)，采用高斯过程来建模形状变化，结合主成分分析等方法，可以用于非刚性配准和基于模型的分割任务。