Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Structural Analysis of Utterances during Guitar
Instruction
Nami Iino1,2 , Hiroya Miura2 , Hideaki Takeda1 ,Masatoshi Hamanaka2 , and Takuichi Nishimura3 ⋆1
National Institute of Informatics
RIKEN Center for Advanced Intelligence Project3
Japan Advanced Institute of Science and Technology
nami-iino@nii.ac.jp
2Abstract. The physical environments for musical instrument instruction include
a mixture of various types of information such as performance sounds and speech.
In our previous study, we analyzed the speech segments of audio data recorded
in real one-on-one classical guitar lessons. In the current work, we annotate twotypes of labels for the teacher’s utterance information and analyze them structurally by applying the Generative Theory of Tonal Music (GTTM) to summarize
the lessons. Our findings revealed a commonality in the interpretation of utterance groupings and demonstrate that the labels for semantically categorizing thecontent of teachers’ utterances are useful in determining the hierarchy.

1

Introduction

In musical instrument lessons, teachers and learners often record audio or video forlater review and reflection. However, these private recordings are rarely made publicly
available as a research resource. Additionally, the specific terms and instructional content utilized in real lessons have not been analyzed in detail. Therefore, we previously
collected sound information from one-on-one classical guitar lessons to help clarify thefeatures of the music teaching-learning process [1].
In the current study, we structurally analyze the utterances that occur during a guitar
lesson with the aim of summarizing the overall lesson. Specifically, We annotated theteacher’s utterances data with two types of labels and generated tree structures using ananalysis method based on the Generative Theory of Tonal Music (GTTM) [2]. Througha comparison of the tree structures by two analysts, we examined the common structural
patterns and rules for the aggregation of tree structures. Our findings revealed a commonality in the analysts’ interpretations and showed that labels related to instructional
content are useful in determining hierarchy.
⋆This study was partially supported by Kayamori Foundation of Informational Science Advancement.

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

A research [3] proposed an interactive information structuring method for meeting
minutes utilizing the GTTM. In that study, to explicitly express the intentions containedin discussions, a tree structure and a method for its extraction were implemented to represent hierarchical importance levels based on verbal and non-verbal information. Our
study aims to find a method suitable for lessons utilizing this same approach. We also
take advantage of CROCUS, a publically available dataset containing performances andtheir written critiques [4]. While this dataset does not provide real-time performance instruction, it is applicable to our study as it focuses on the instructor’s “words.”

2

Data Preparation

Features of Audio Segment of Classical Guitar Lessons In our previous study, we
collected audio data from a pair (teacher and student) of one-on-one guitar lessons
and categorized them into groups based on musical pieces for which the lessons had
been given at least three times [1]. We then manually transcribed the utterances foreach teacher and student and segmented them on the basis of the time interval between
utterances.
Our analysis of the segmented data showed that (i) the percentage of students’ utterances tended to decrease as the number of lessons progressed, and (ii) the percentageof teachers’ utterances did not change much. These findings suggest that a more detailed analysis focusing on teachers’ utterances might make it possible to extract themain points of the lesson. In addition, an integrated analysis of audio segments and thecontent of utterances is necessary to achieve a comprehensive summary of the lesson.
Annotation of Types of Teacher’s Utterance In this study, we focused on the teacher’sutterances and annotated two types of labels representing instructional information withthe transcribed data. Through these annotations, it is possible to clarify the type ofinstructions given at specific times during a musical lesson.
– Instructional Topic Labels: The Music Teacher’s Ontology [5] is a knowledge system for music education that represents a hierarchy of topics on which teachers mightprovide feedback to students. We referred to this ontology to define instructional topiclabels. First, we defined four upper-level labels: Musical piece, regarding the musical style of the piece (period, musical form, etc.), Musical expression, regarding theintentional use of expression by the teacher or learner, Technique, regarding physicaltechnique, and Other, regarding performance, mental aspects and so on. Then, we defined 18 specific topics as lower-level labels, such as Tempo, Rhythm, Fingering, andArticulation. We annotated one upper-level label and two or fewer lower-level labels
for each unit of utterance.
– Instructional Content Labels: SOAP is a classification framework that takes into account the semantic elements of sentences for natural language [6]. It was originally designed for scientifically describing the thought processes of doctors in medical records
using natural sentences. In this study, by referring to research [4], we adapted the classification categories of SOAP to the field of music as follows:– Subjective data (S): teacher providing general and/or specific conceptual information based on subjectivity.

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Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

– Objective data (O): teacher providing general and/or specific conceptual information based on objectively referable events or concepts.
– Assessment (A): teacher’s evaluation of a student’s applied and/or conceptual knowledge.
– Plan (P): giving a specific opinion or recommendation to guide the student’s action
towards the achievement of a specific musical aims.
We pointed that the four items can characterize the instructional content and annotated them to the teacher’s utterances. If an utterance had more than one label, we
provided two or more annotations.

3

Structural Analysis of Teacher’s Utterances

Generation of Tree Structures using the GTTM The Generative Theory of Tonal
Music (GTTM) is a musical framework that can generate a tree structure with a hierarchical temporal organization and a principal-subordinate relationship of branches. Akey feature of GTTM is that it enables somewhat subjective musical analysis because it
allows for different interpretations based on the analyst’s judgment while still adheringto the basic rules. It also enables “reduction,” which extracts abstracted groups from theupper layers of the tree structure. These mean that GTTM can be utilized to reveal theanalyst’s perspective and identify structuring rules for summarizing the lesson.
In this study, two researchers independently generated a tree structure. First, wetook one of one lesson data described in previous section and divided the teacher’sutterances into five sections based on the segments and the content of the utterances.
Next, we applied the time-span analysis method defined in the GTTM for grouping andhierarchization.
Comparison of Tree Structures and Discussion Figure 1 shows an example of utterances, annotated labels, and two generated tree structures, where structures A and B
correspond to the results of analysts A and B. As we can see, the groupings of the twotree structures tended to be similar. Specifically, the branches were divided into threegroups, suggesting a commonality in the analysts’ perceptions of speech cohesion. On
the other hand, there were differences in the hierarchy within the groups. This was dueto the fact that (i) analyst A was conscious of summarization and placed S in the upperlevel labels of the instructional content labels, representing the problems of the student’s
performance, whereas (ii) analyst B focused on the number of low-level labels and thelatter half of the utterance.
The breakdown of instructional content labels in the five sections was S: 16.7%,
O: 18.6%, A: 2.1%, P: 37.5%, S&A: 2.1%, and None: 22.9%. The fact that about 23%of the utterances could not be annotated (None) indicates that semantically significant
utterances were limited. Examples of such unannotated utterances include rhythmic
counting and responses using short words phrases as “I see.” Furthermore, P tended tobe located at a higher level of hierarchy in both structures. These results indicate that
instructional content labels are useful for identifying hierarchies.
We need to collect additional tree structure data because the samples in this study
were very small. Additionally, we need to define aggregation rules for the tree structures by applying GTTM: specifically, (1) Grouping Preference Rules, which group694

Proc. of the 16th International Symposium on CMMR, Tokyo, Japan, Nov. 13-17, 2023

Instructional topic label
Instructional
Utterances
Upper-level
Low-level Low-level content label
Teacher Yes, about this much. One-two-three.
Musical expression Tempo
Teacher Well, you tend to rush with the portamento. Musical expression Tempo
Rendition
STeacher Yes.
Musical expression Tempo
Rendition
Please keep the sound properly to the note
TeacherMusical expression Rendition Note value
Pvalue. This too.
Student Oh, I see.
Teacher Also, it is fast after “ti.”Musical expression Tempo
STeacher Keep the note value tight.
Musical expression Note value
PYes, I think that would make it in time. YouTeacher
Musical expression Tempo
S!Aare rushing too much about this.

SpeakerYes, around here, I'm in a hurry, so myStudent
performance is getting squished, here.
Teacher Flop.
Musical expression
Teacher Yes, play each note properly.
Musical expression
I see. My performance is getting more andStudent
more messed up here.
Yeah, don't rush. As you get better at
Teacher playing it, the speed gets faster. So, you
Otherhave to be careful.

Tempo
Tempo

Tempo

Note value! Tree structure A--- Tree structure BPAFig. 1. Example of utterance data and tree structures.

utterances based on measures such as utterance duration, interval, and the number ofutterances, and (2) Significance Preference Rules, which identify important utterances
based on key words and label information.

4Conclusion

In this paper, we performed a structural analysis of teacher’s utterances to summarize
the content of musical instrument lessons. First, we annotated the transcribed data ofa classical guitar one-on-one lesson with two types of semantic labels. Then, we hierarchically structured the utterances based on the GTTM. As a result, we were able
to clarify the analyst’s perspective and examine the issues with the structuring rules. Infuture work, we will expand the data and conduct a more in-depth analysis of individualdata. This will enable us to apply the findings to support student reflection and improve
the quality of teaching.

References
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4. Matsubara, M., Kagawa, R., Hirano, T., and Tsuji, I.: CROCUS: Dataset of Musical Performance Critiques Relationship between Critique Content and Its Utility, Proc. of the 15th
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