A common way of learning to perform a task is to observe how it is carried
out by experts. However, it is well known that for most tasks there is no
unique way to perform them. This is especially noticeable the more complex the
task is because factors such as the skill or the know-how of the expert may
well affect the way she solves the task. In addition, learning from experts
also suffers of having a small set of training examples generally coming from
several experts (since experts are usually a limited and expensive resource),
being all of them positive examples (i.e. examples that represent successful
executions of the task). Traditional machine learning techniques are not useful
in such scenarios, as they require extensive training data. Starting from very
few executions of the task presented as activity sequences, we introduce a
novel inductive approach for learning multiple models, with each one
representing an alternative strategy of performing a task. By an iterative
process based on generalisation and specialisation, we learn the underlying
patterns that capture the different styles of performing a task exhibited by
the examples. We illustrate our approach on two common activity recognition
tasks: a surgical skills training task and a cooking domain. We evaluate the
inferred models with respect to two metrics that measure how well the models
represent the examples and capture the different forms of executing a task
showed by the examples. We compare our results with the traditional process
mining approach and show that a small set of meaningful examples is enough to
obtain patterns that capture the different strategies that are followed to
solve the tasks.

通过学习少量执行任务的示例，我们引入了一种归纳性方法来学习多种模型，每个模型都代表一种执行任务的替代策略。我们将此方法应用于手术技能训练和烹饪领域的常见活动识别任务，并通过评估模型是否能够代表示例并捕捉执行任务的不同形式来验证其有效性。研究结果表明，一小组有意义的示例足以获得捕捉解决任务的不同策略的模式。

学习任务的替代方法

Learning Alternative Ways of Performing a Task

The present study has two goals relating to the grammar of prosody,
understood as the rhythms and melodies of speech. First, an overview is
provided of the computable grammatical and phonetic approaches to prosody
analysis which use hypothetico-deductive methods and are based on learned
hermeneutic intuitions about language. Second, a proposal is presented for an
inductive grounding in the physical signal, in which prosodic structure is
inferred using a language-independent method from the low-frequency spectrum of
the speech signal. The overview includes a discussion of computational aspects
of standard generative and post-generative models, and suggestions for
reformulating these to form inductive approaches. Also included is a discussion
of linguistic phonetic approaches to analysis of annotations (pairs of speech
unit labels with time-stamps) of recorded spoken utterances. The proposal
introduces the inductive approach of Rhythm Formant Theory (RFT) and the
associated Rhythm Formant Analysis (RFA) method are introduced, with the aim of
completing a gap in the linguistic hypothetico-deductive cycle by grounding in
a language-independent inductive procedure of speech signal analysis. The
validity of the method is demonstrated and applied to rhythm patterns in
read-aloud Mandarin Chinese, finding differences from English which are related
to lexical and grammatical differences between the languages, as well as
individual variation. The overall conclusions are (1) that normative
language-to-language phonological or phonetic comparisons of rhythm, for
example of Mandarin and English, are too simplistic, in view of diverse
language-internal factors due to genre and style differences as well as
utterance dynamics, and (2) that language-independent empirical grounding of
rhythm in the physical signal is called for.

本文主要研究语音节奏（prosody）的语法问题，提出了一种基于从低频信号中推导出的语音节奏结构的归纳方法，即 Rhythm Formant Theory 和 Rhythm Formant Analysis 方法，并以朗读的汉语为例，探究了汉语和英语的差异，并得出结论：语言内部的基准化语音或语音对比对于语音的差异性过于简单，需要从物理信号中的语音节奏来研究语音的差异以找到更加准确的结果。