Department Publications

With the recent advancement of Large Language Models (LLMs), environmental perception methods, and frameworks for simplifying robot functionality implementation, robotic receptionists present an exciting opportunity to minimize humanrelated errors. However, most existing humanoid robots are costly and overqualified for such tasks, as they are primarily designed for research applications. To address this, we introduce a standalone, offline, cost-effective smart mobile robot receptionist built on an edge device that efficiently performs a predefined set of tasks while utilizing minimal hardware and computational resources. Our robot receptionist can engage in smooth conversations, recognize and interact with both new and returning users, perform hand gestures, provide navigation assistance through verbal and physical guidance, and autonomously navigate while avoiding static and dynamic obstacles …

This paper presents the development of a Sinhala Speech Recognition System to be deployed in an Interactive Voice Response (IVR) system of a telecommunication service provider. The main objectives are to recognize Sinhala digits and names of Sinhala songs to be set up as ringback tones. Sinhala being a phonetic language, its features are studied to develop a list of 47 phonemes. A continuous speech recognition system is developed based on Hidden Markov Model (HMM). The acoustic model is trained using the voice through mobile phone. The outcome is a speaker independent speech recognition system which is capable of recognizing 10 digits and 50 Sinhala songs. A word error rate (WER) of 11.2% using a speech corpus of 0.862 hours and a sentence error rate (SER) of 5.7% using a speech corpus of 1.388 hours are achieved for digits and songs respectively.

Precise temporal localization of named entities in speech is crucial for privacy-preserving audio processing. However, prevailing cascaded pipelines propagate transcription errors and end‐to‐end models lack the temporal granularity required for reliable frame‐level detection. To address these limitations, we introduce DEnSNEL (Dense End‐to‐end Spoken Named Entity Localizer), the first end-to-end model to perform direct, frame-level spoken named entity localization, without intermediate character or text representations. We reformulate the task as a dense, frame-wise binary classification ("entity" vs. "non-entity"), employing a lightweight encoder-classifier architecture. To improve boundary delineation, DEnSNEL incorporates a learnable complex filter bank to capture phonetic information, and employs a boundary-focused loss that explicitly optimizes span precision. On the SLUE Phase 2 benchmark …