PRAAT Software; Utilization of Computerized Approach for Determination of Variation Present in Recorded Audios from Distinct Sources

Rawat, Smriti; Khan, Tasbiha; Chauhan, Amit

doi:10.14445/22315381/ijett-v67i3p221

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…New rhythmic features are put forward to the analysis of relevant discussions in broadcasting, such as vocal expression features, discourse stress, parataxis, and compound rhythmic phrases, and they are integrated into rhythm and text annotation specifications in database [1]. At present, PRAAT software is commonly used to analyze recorded audio with the aid of a computer [2]. In addition, the relationship between vocal timbre and emotional perception in words is also researched, and the results show vocal timbre can convey the emotional meaning [3].…”

Section: Introductionmentioning

confidence: 99%

“…(1) Based on the broadcasting knowledge and Chinese phonetic characteristics, a database of broadcasting vocal timbre was constructed and annotated. (2) Combining the mechanism of human vocalization and PCA technology, we screened out the characteristic parameters of broadcast timbre and divided them into three categories which include source parameters, vocal tract parameters, and human hearing parameters. (3) We conducted fusion and nonfusion experiments on the three types of timbre features, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Objective Evaluation Method of Broadcasting Vocal Timbre Based on Feature Selection

Lan

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Broadcasting voice is used to convey ideas and emotions. In the selection process of broadcasting and hosting professionals, the vocal timbre is an important index. The subjective evaluation method is widely used, but the selection results have certain subjectivity and uncertainty. In this paper, an objective evaluation method of broadcasting vocal timbre is proposed. Firstly, the broadcasting vocal timbre database is constructed on Chinese phonetic characteristics. Then, the timbre feature selection strategy is presented based on human vocal mechanism, and the broadcast timbre characteristics are divided into three categories, which include source parameters, vocal tract parameters, and human hearing parameters. Finally, the three models of hidden Markov model (HMM), Gaussian Mixture Model-General Background Model (GMM-UBM), and long short-term memory (LSTM) are exploited to evaluate the timbre of the broadcast by extracting timbre features and four timbre feature combinations. The experiments show that the selection of timbre features is scientific and effective. Moreover, the accuracy of the LSTM network using the deep learning algorithm in the objective evaluation of the broadcast timbre is better than the traditional HMM and GMM-UBM, and the proposed method can achieve about 95% accuracy rate in our database.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%