50 citations · 53 across the 14 of their papers we have counts for
15 papers
SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models
Thomas Thebaud, Yuzhe Wang, Hao Zhang +5
Streaming speech-to-speech language models aim to answer spoken queries directly with synthetic speech. However, standard speech and text benchmarks do not capture whether these sy…
TurnNat: Automatic Evaluation of Turn-Taking Naturalness in Dyadic Spoken Dialogue
Hao Zhang, Thomas Thebaud, Georgi Tinchev +2
Turn-taking naturalness is central to full-duplex spoken dialogue systems, yet its automatic evaluation remains limited. Existing evaluations often rely on human judgments or behav…
TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech
Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler +5
With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important. Emotional entrainment is shaped by…
Reference-Based Prosody and Rhythm Evaluation for Spoken Dialogue Systems
Ashish Hallur, Thomas Thebaud, Georgi Tinchev +2
Speech-to-speech (S2S) AI agents are advancing rapidly, yet evaluation lacks interpretable speech-native measures for conversational prosody and rhythm. Because , speaking rat…
StanceBench: A Benchmark for Audio LLM-Based Interpersonal Stance Evaluation from Speech
Yuzhe Wang, Thomas Thebaud, Jennifer Hu +5
Speech-to-speech dialogue models increasingly depend on prosody and interactional nuance to convey social intent, yet benchmarks for these cues remain limited. We introduce StanceB…
Cross-lingual Knowledge Distillation via Flow-based Voice Conversion for Robust Polyglot Text-To-Speech
Dariusz Piotrowski, Renard Korzeniowski, Alessio Falai +5
In this work, we introduce a framework for cross-lingual speech synthesis, which involves an upstream Voice Conversion (VC) model and a downstream Text-To-Speech (TTS) model. The p…
InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning
Lintong Zhang, Tejaswi Digumarti, Georgi Tinchev +1
Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which ope…
Real-time LIDAR localization in natural and urban environments
Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
Localization is a key challenge in many robotics applications. In this work we explore LIDAR-based global localization in both urban and natural environments and develop a method s…
Modelling low-resource accents without accent-specific TTS frontend
Georgi Tinchev, Marta Czarnowska, Kamil Deja +2
This work focuses on modelling a speaker's accent that does not have a dedicated text-to-speech (TTS) frontend, including a grapheme-to-phoneme (G2P) module. Prior work on modellin…
Universal Neural Vocoding with Parallel WaveNet
Yunlong Jiao, Adam Gabrys, Georgi Tinchev +3
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder. Our universal vocoder offers real-time high-quality s…
Resolution Correspondence Networks
Georgi Tinchev, Shuda Li, Kai Han +2
In this paper, we aim at establishing accurate dense correspondences between a pair of images with overlapping field of view under challenging illumination variation, viewpoint cha…
Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure
Milad Ramezani, Georgi Tinchev, Egor Iuganov +1
In this paper, we present a factor-graph LiDAR-SLAM system which incorporates a state-of-the-art deeply learned feature-based loop closure detector to enable a legged robot to loca…
SKD: Keypoint Detection for Point Clouds using Saliency Estimation
Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
We present SKD, a novel keypoint detector that uses saliency to determine the best candidates from a point cloud for tasks such as registration and reconstruction. The approach can…
Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU
Georgi Tinchev, Adrian Penate-Sanchez, Maurice Fallon
Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail…
Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments
Georgi Tinchev, Simona Nobili, Maurice Fallon
In this work we introduce Natural Segmentation and Matching (NSM), an algorithm for reliable localization, using laser, in both urban and natural environments. Current state-of-the…