Gouravarapu Shanmukha Sai Keerthi supervised by Dr. Kavita Vemuri  received her Master of Science – Dual Degree  in Computer Science and Engineering (CSD). Here’s a summary of her research work on Intelligibility Estimation and Articulatory Subsystem Analysis for Telugu Post-Stroke Dysarthric Speech: 

Stroke often results in motor speech disorders such as dysarthria, leading to reduced speech intelligibility and impaired communication. Accurate assessment of speech intelligibility and identification of underlying articulatory deficits are essential for effective rehabilitation. However, existing approaches largely rely on subjective evaluation or global scoring, offering limited support for targeted therapy planning, particularly in under-resourced languages such as Telugu. This work presents an objective and interpretable framework for assessing speech intelligibility and articulatory impairments in Telugu-speaking post-stroke individuals. A phonemically structured speech corpus was developed, comprising recordings from 13 stroke participants along with perceptual intelligibility ratings obtained from human evaluators. In addition, speech data from 25 age-matched healthy participants were collected as part of a pilot study to establish a reference dataset. Word-level intelligibility was estimated using Dynamic Time Warping (DTW) applied to embeddings derived from the Wav2Vec2-LargeXLSR-53-Telugu model, and validated against listener judgments. To enable detailed analysis beyond global scoring, a minimal pairs based articulatory mapping framework was proposed. By analyzing phonological contrasts such as place of articulation and voicing, the framework identifies consistent phoneme-level errors and maps them to probable articulatory subsystems. This approach provides a meaningful link between acoustic speech deviations and underlying physiological mechanisms. In addition, a combined analysis of word-level and passage-level speech was performed to capture both articulatory precision and performance in conversational speech. The results highlight the heterogeneous nature of post-stroke speech impairment, with deficits often localized to specific phoneme groups or arising from coordination challenges in continuous speech. The findings also emphasize the importance of considering dialectal variation in speech assessment. Beyond overall intelligibility assessment, the proposed framework enables phoneme-level analysis of speech errors. By identifying consistently misarticulated phonemes and mapping them to underlying articulatory subsystems, it provides interpretable insights that can support targeted therapy planning. In summary, proposed approach employs phonemic-level assessment to identify speech impairments and provides articulator specific insights, enabling therapists to design targeted interventions using phonetic-level measures. This approach contributes toward the development of data driven and personalized rehabilitation strategies for dysarthric speech in Telugu. 

May 2026