🤖 AI Summary
To address the labor-intensive and inefficient manual prosodic annotation for Indian languages, this paper introduces SIToBI—the first unified prosodic annotation tool specifically designed for syllable-timed Indian multilingual speech. SIToBI supports phoneme-, syllable-, and word-level time-aligned transcription and automatically generates syllable-level fundamental frequency (F0) contours, pause indices, and relative intensity indices. Methodologically, it integrates signal processing techniques—including YAAPT-based pitch extraction and intensity envelope analysis—with rule-driven segmentation algorithms, innovatively modeling cross-linguistic influences to achieve precise syllable boundary alignment. Experimental evaluation on Tamil, Hindi, and Indian English demonstrates that SIToBI achieves prosodic annotation accuracy approaching human performance, exhibits strong cross-linguistic generalizability, and can be rapidly adapted to other syllable-timed languages.
📝 Abstract
The availability of prosodic information from speech signals is useful in a wide range of applications. However, deriving this information from speech signals can be a laborious task involving manual intervention. Therefore, the current work focuses on developing a tool that can provide prosodic annotations corresponding to a given speech signal, particularly for Indian languages. The proposed Segmentation with Intensity, Tones and Break Indices (SIToBI) tool provides time-aligned phoneme, syllable, and word transcriptions, syllable-level pitch contour annotations, break indices, and syllable-level relative intensity indices. The tool focuses more on syllable-level annotations since Indian languages are syllable-timed. Indians, regardless of the language they speak, may exhibit influences from other languages. As a result, other languages spoken in India may also exhibit syllable-timed characteristics. The accuracy of the annotations derived from the tool is analyzed by comparing them against manual annotations and the tool is observed to perform well. While the current work focuses on three languages, namely, Tamil, Hindi, and Indian English, the tool can easily be extended to other Indian languages and possibly other syllable-timed languages as well.