## 4 Experiments
### Datasets
#### 4.1.2 PSC-PS-DF  
To address the issue of a lack of resources for Chinese disfluent speech data, this paper used the construction method of the SEP-28k dataset as a reference to construct the Chinese disfluent speech dataset PSC-PS-DF. PSC-PS-DF consists of propositional speaking files for the Mandarin Proficiency Test (Putonghua Shuiping Ceshi, PSC). Previous work has shown that, due to the nature of Chinese propositional speaking, which requires the speaker to freely describe a topic for three minutes without any reference text, the speech files contain a large number of disfluent features, such as \"um\", \"ah\", and \"uh\" interjections, blocks, prolongations, and repetition, but such disfluent features are rarely marked and used in research [55]. In this study, disfluent features were annotated in Chinese propositional speaking data to obtain spontaneously spoken disfluent features in Chinese for the detection of disfluent Chinese-language speech. A total of 4414 3 s disfluent segments were extracted from 400 propositional speaking data in the PSC-PS-DF dataset. The disfluent segments were labeled by three annotators with four types of disfluencies, namely WP, Pro, Intrj, and Bl, whose definitions are the same as those in the SEP-28k dataset and can be referred to in Table 1. The Chinese dataset does not include the annotation of Snd, and the repetition of Chinese speech is limited to the annotation of repetitive words and phrases, which are uniformly annotated with WP. The four types of disfluent annotations for the PSC-PS-DF dataset can be visualized by referring to Figure 2. Fluent segments in the PSC-PS-DF dataset are composed of speech unanimously identified by the three annotators as having a fluency level of level 1 in the propositional speaking assessment results. The length of all audio segments in the PSC-PS-DF was set to 3 s, with a sampling frequency of 16 kHz. The data distribution is shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline
**Disfluency Labels** & **Definition** & **Examples** \\ \hline Sound repetitions (Snd) & Repetitions of syllables & I (wh-wh-) whispered a secret \\ Word repetitions (WP) & Repetitions of words & I know (know) a secret \\ Prolongations (Pro) & Extended syllables & I kn(nnnnn)ow \\ Interjections (Intri) & Filler words or non-words & I (um) know a (uh) secret \\ Blocks (Bl) & Long stuttered pauses & I know (pause) a secret \\ \hline \end{tabular}
\end{table} 
Table 1: Definition and examples of different speech disfluencies in the SEP-28k dataset. When annotating disfluent features in PSC-PS-DF, researchers labeled common words such as \"um\", \"ah\", \"uh\", and \"well\" as interjections, while word repetition generally occurred when the speaker hesitated, supplemented, or modified their speech. As there is no strict definition for blocks, this study followed [PERSON]'s [56] research viewpoint and uses 0.3 s as the boundary for speech pauses within or between sentences. In addition, this study uses 0.8 s as the boundary for prolongations. When annotating Chinese disfluent features, researchers extract blocks longer than 0.3 s and prolongations longer than 0.8 s as disfluent markers with the labels Bl and Pro, respectively. This study also analyzes Chinese disfluent features, and Figures 3-10 show the distribution of their speech waveforms and the speech spectrograms of the four types of features. The different disfluency features can be more easily identified by analyzing the speech waveforms and speech spectrograms for the five types of disfluencies. Figures 3-10 depict the waveforms (amplitude vs. time) on the left and a time-frequency plot of the wavelet decomposition using these data on the right. From the plots, we can observe that in the PSC-PS-DF dataset, the WP feature is represented by multiple identical speech waveforms, which exhibit shorter articulation time and narrower speech waveforms for words. The Pro feature in the plots exhibits a fuller time-domain waveform with longer articulation durations for single words and syllables.