SearcharxivSearch

arXiv · 2502.09661

AutoProsody: A Prosodic Feature Extraction Tool for Indian 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.

Explore related subjects

Keep this discovery

BibTeXRIS

Preethi Thinakaran, Malarvizhi Muthuramalingam, Sooriya S, Anushiya Rachel Gladston, P. Vijayalakshmi, Hema A Murthy, T. Nagarajan. 2025-02-12. AutoProsody: A Prosodic Feature Extraction Tool for Indian Languages. https://arxiv.org/abs/2502.09661

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD