Abstract
This chapter will present a study that explored speech expressiveness and information arrangement in relation to discourse prosody in continuous Mandarin speeches. Based on corpus data from four diverse speech genres, our study examined perceived prosodic highlights in correlation with speech expressiveness and the convergence of prominence patterns for discourse prosody. Using the corpus linguistic approach and quantitative analyses, we first summarized the number of perceived emphasis token patterns and their distribution across speech genres. Then, we conducted two experiments: (i) speech expressiveness by information weighting calculation that is based on prosodic highlights allocation and (ii) discourse prosody through the convergence of patterned prosodic highlights in limited degrees of contrastive strength. The results from the first experiment pinpointed major differences across speech genres in terms of expressiveness, demonstrating that the most spontaneous type of speech carried the largest amount of information. The second experiment found that a limited number of intonation patterns converged for higher-level discourse prosody. Ultimately, our research uncovered the sources contributing to speech expressiveness and diversity across speech genres, while at the same time showed successful convergence of divergent surface variations from speech signals to deduce systematic and predictable patterns of discourse-level global prosody.
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Notes
- 1.
The term “prosodic highlights” in our study refers to the prosody-related prominence through perception, which is based on the speech context. Prosodic highlights are defined in terms of features in perceivable higher/lower pitch and/or relatively stronger/weaker loudness, as well as degrees of contrast (see Sect. 24.2.2. for the definitions of annotating levels of perceived emphases). In this chapter, we use perceived prosodic highlights, perceived prominence, and emphasis interchangeably.
- 2.
The hierarchical relationship of discourse-prosodic units is presented in the spirit of the hierarchical prosodic phrase grouping (HPG) framework (Tseng 鄭秋豫 2010; Tseng et al. 2005a, 2005b; Tseng and Su 2008) that our study adopted when annotating the discourse-prosodic units/boundaries in the continuous speeches. Please refer to Sect. 24.2.2 for a brief introduction of the framework.
- 3.
More precisely, phrasal-level discourse-prosodic units refer to prosodic phrase units (PPh) in the HPG framework. Please refer to Sect. 24.2.2 for further details.
- 4.
We adopted the term “variation” not with its traditional sense in linguistics but rather in the sense from music studies; thus, it is more similar to the concept of melodic variations.
- 5.
- 6.
As explained by Tseng (2013), in utilizing the HPG framework for the annotation of discourse-prosodic units, the main strength is that such a framework is not text-bounded, nor is it syntactically predetermined. While the framework purposely distances itself away from the possible connotations associated with other levels of linguistic information (Tseng 2013), it pays further attention to units of higher discourse-prosodic levels. Since the main focus of our study was to capture the features of speech expressiveness and discourse prosody, it was essential that we incorporated such a framework, which takes into consideration prosodic features from units whose size reaches beyond that of the sentential level.
- 7.
As will be shown in Sect. 24.4.1, the chunking sizes at different discourse-prosodic levels differed drastically across speech genres. This is the main reason that we had to remove the length effect.
- 8.
Again, we used the term “variation” in the sense of melodic variation in music studies.
- 9.
Figure 24.2b demonstrates the “letter assignment” in Step Three. We took the first four PPhs in Figure 24.2a to illustrate the procedure of the letter assignment. As explained, whenever the following PPh corresponded to a different ET pattern, a new letter was assigned. As a result, the final alphabetic sequence was not limited to merely A/B combinations as illustrated in Figure 24.2b, as more complex patterns did exist.
- 10.
- 11.
In Tseng and Su (2012), the speech data incorporated in their analyses included the two read speech genres CNA and WB, as well as the spontaneous lecture SpnL. However, the degree of prominence levels did not cover reduction E0 in their study.
- 12.
In their findings, Tseng and Su (2012) suggested that the six major ET patterns are (1) “E1”; (2) “E2 E1”; (3) “E1 E2 E1”; (4) “E1 E2”; (5) “E2”; and (6) “E2 E1 E2”. The cross-speech genre analyses showed that CNA and WB were further distinguished by the “E2 E1” and “E1 E2” patterns, respectively, whereas the lecture data was dominated by the “E1” pattern (Tseng and Su 2012).
- 13.
In Tseng and Su (2012), it was shown that the pattern “E1” only took up about 10% of the total ET patterns in CNA and WB, compared to about 39% of the “E1” pattern found in the total ET patterns in the SpnL data.
- 14.
The calculation of the weighting scores of information allocation was based on normalized BG/PG units.
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Chen, H.KY., Tseng, CY. (2023). Perception Correlated Information Allocation and Pattern Convergence for Discourse Prosody. In: Huang, CR., Hsieh, SK., Jin, P. (eds) Chinese Language Resources. Text, Speech and Language Technology, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-031-38913-9_24
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