32 citations · 69 across the 14 of their papers we have counts for
16 papers
Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
Goeric Huybrechts, Srikanth Ronanki, Sai Muralidhar Jayanthi +2
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the p…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Wanda++: Pruning Large Language Models via Regional Gradients
Yifan Yang, Kai Zhen, Bhavana Ganesh +11
Large Language Models (LLMs) pruning seeks to remove unimportant weights for inference speedup with minimal accuracy impact. However, existing methods often suffer from accuracy de…
Zero-resource Speech Translation and Recognition with LLMs
Karel Mundnich, Xing Niu, Prashant Mathur +10
Despite recent advancements in speech processing, zero-resource speech translation (ST) and automatic speech recognition (ASR) remain challenging problems. In this work, we propose…
Adaptive Video Understanding Agent: Enhancing efficiency with dynamic frame sampling and feedback-driven reasoning
Sullam Jeoung, Goeric Huybrechts, Bhavana Ganesh +2
Understanding long-form video content presents significant challenges due to its temporal complexity and the substantial computational resources required. In this work, we propose…
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models
Raghuveer Peri, Sai Muralidhar Jayanthi, Srikanth Ronanki +11
Integrated Speech and Large Language Models (SLMs) that can follow speech instructions and generate relevant text responses have gained popularity lately. However, the safety and r…
DCTX-Conformer: Dynamic context carry-over for low latency unified streaming and non-streaming Conformer ASR
Goeric Huybrechts, Srikanth Ronanki, Xilai Li +3
Conformer-based end-to-end models have become ubiquitous these days and are commonly used in both streaming and non-streaming automatic speech recognition (ASR). Techniques like du…
Dynamic Chunk Convolution for Unified Streaming and Non-Streaming Conformer ASR
Xilai Li, Goeric Huybrechts, Srikanth Ronanki +2
Recently, there has been an increasing interest in unifying streaming and non-streaming speech recognition models to reduce development, training and deployment cost. The best-know…
Low-data? No problem: low-resource, language-agnostic conversational text-to-speech via F0-conditioned data augmentation
Giulia Comini, Goeric Huybrechts, Manuel Sam Ribeiro +2
The availability of data in expressive styles across languages is limited, and recording sessions are costly and time consuming. To overcome these issues, we demonstrate how to bui…
Voice Filter: Few-shot text-to-speech speaker adaptation using voice conversion as a post-processing module
Adam Gabryś, Goeric Huybrechts, Manuel Sam Ribeiro +6
State-of-the-art text-to-speech (TTS) systems require several hours of recorded speech data to generate high-quality synthetic speech. When using reduced amounts of training data,…
Cross-speaker style transfer for text-to-speech using data augmentation
Manuel Sam Ribeiro, Julian Roth, Giulia Comini +3
We address the problem of cross-speaker style transfer for text-to-speech (TTS) using data augmentation via voice conversion. We assume to have a corpus of neutral non-expressive d…
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
Raahil Shah, Kamil Pokora, Abdelhamid Ezzerg +5
Whilst recent neural text-to-speech (TTS) approaches produce high-quality speech, they typically require a large amount of recordings from the target speaker. In previous work, a 3…
EmoCat: Language-agnostic Emotional Voice Conversion
Bastian Schnell, Goeric Huybrechts, Bartek Perz +2
Emotional voice conversion models adapt the emotion in speech without changing the speaker identity or linguistic content. They are less data hungry than text-to-speech models and…
Low-resource expressive text-to-speech using data augmentation
Goeric Huybrechts, Thomas Merritt, Giulia Comini +3
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired s…
Voice Conversion for Whispered Speech Synthesis
Marius Cotescu, Thomas Drugman, Goeric Huybrechts +2
We present an approach to synthesize whisper by applying a handcrafted signal processing recipe and Voice Conversion (VC) techniques to convert normally phonated speech to whispere…
Traditional Machine Learning for Pitch Detection
Thomas Drugman, Goeric Huybrechts, Viacheslav Klimkov +1
Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent articles have proposed deep learning for robust pitch tracking…