18 citations · 58 across the 30 of their papers we have counts for
32 papers
Input Representation, Not Model Size or Pretraining Scale, Shapes Speaker Verification Robustness to Temporal Degradation
Erdem Baha Topbas, Srikanth Madikeri, Elisa Pellegrino +1
Speech remains intelligible to listeners under heavy temporal degradation, but whether speaker identity survives is largely unexplored. We evaluate six automatic speaker verificati…
Unified Shared Encoder in Spoof-Aware Speaker Verification with Hybrid Wavelet Prompt Tuning
Aref Farhadipour, Srikanth Madikeri, Teodora Vukovic +2
Spoofing-aware speaker verification (SASV) must confirm who is speaking and that the speech is genuine, but current systems gain one at the expense of the other. Modular cascades a…
The Visual Bottleneck: Sparse-Frame Adaptation of MLLMs for Joint Spatial-Temporal Video Grounding
Jiameng Zhang, Srikanth Madikeri
Large-scale video platforms process millions of uploads hourly, requiring moderation systems that can localize when and where policy violations occur within each video. Processing…
How to Leverage Synthetic Speech for LLM-Based ASR Systems?
Yanis Labrak, Dairazalia Sanchez-Cortes, Sergio Burdisso +9
In regulated domains such as banking and healthcare, where privacy constraints make real speech costly to collect and retain, synthetic speech from modern text-to-speech (TTS) is a…
Abstract Compression of Conversational Audio Context for LLM-based ASR
Shashi Kumar, Esaú Villatoro-Tello, Sergio Burdisso +7
Standard LLM-based speech recognition systems process utterances in isolation, limiting their ability to leverage conversational context. Here we study whether multimodal context f…
TidyVoice 2026 Challenge Evaluation Plan
Aref Farhadipour, Jan Marquenie, Srikanth Madikeri +6
The performance of speaker verification systems degrades significantly under language mismatch, a critical challenge exacerbated by the field's reliance on English-centric data. To…
Reducing Prompt Sensitivity in LLM-based Speech Recognition Through Learnable Projection
Sergio Burdisso, Esaú Villatoro-Tello, Shashi Kumar +7
LLM-based automatic speech recognition (ASR), a well-established approach, connects speech foundation models to large language models (LLMs) through a speech-to-LLM projector, yiel…
Adaptive Multimodal Person Recognition: A Robust Framework for Handling Missing Modalities
Aref Farhadipour, Teodora Vukovic, Volker Dellwo +2
Person identification systems often rely on audio, visual, or behavioral cues, but real-world conditions frequently present with missing or degraded modalities. To address this cha…
Spoofing-Aware Speaker Verification via Wavelet Prompt Tuning and Multi-Model Ensembles
Aref Farhadipour, Ming Jin, Valeriia Vyshnevetska +3
This paper describes the UZH-CL system submitted to the SASV section of the WildSpoof 2026 challenge. The challenge focuses on the integrated defense against generative spoofing at…
TidyVoice: A Curated Multilingual Dataset for Speaker Verification Derived from Common Voice
Aref Farhadipour, Jan Marquenie, Srikanth Madikeri +1
The development of robust, multilingual speaker recognition systems is hindered by a lack of large-scale, publicly available and multilingual datasets, particularly for the read-sp…
Text-only adaptation in LLM-based ASR through text denoising
Andrés Carofilis, Sergio Burdisso, Esaú Villatoro-Tello +8
Adapting large language model (LLM)-based automatic speech recognition (ASR) systems to new domains using text-only data is a significant yet underexplored challenge. Standard fine…
CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation
Aref Farhadipour, Shiran Liu, Masoumeh Chapariniya +4
The CL-UZH team submitted one system each for the fixed and open conditions of the NIST SRE 2024 challenge. For the closed-set condition, results for the audio-only trials were ach…
TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Shashi Kumar, Srikanth Madikeri, Esaú Villatoro-Tello +8
Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets a…
Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
Andres Carofilis, Pradeep Rangappa, Srikanth Madikeri +10
Fine-tuning pretrained ASR models for specific domains is challenging when labeled data is scarce. But unlabeled audio and labeled data from related domains are often available. We…
Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering
Pradeep Rangappa, Andres Carofilis, Jeena Prakash +10
Fine-tuning pretrained ASR models for specific domains is challenging for small organizations with limited labeled data and computational resources. Here, we explore different data…
Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction
Sergio Burdisso, Srikanth Madikeri, Petr Motlicek
Efficiently deriving structured workflows from unannotated dialogs remains an underexplored and formidable challenge in computational linguistics. Automating this process could sig…
TokenVerse: Towards Unifying Speech and NLP Tasks via Transducer-based ASR
Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez +6
In traditional conversational intelligence from speech, a cascaded pipeline is used, involving tasks such as voice activity detection, diarization, transcription, and subsequent pr…
XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models
Shashi Kumar, Srikanth Madikeri, Juan Zuluaga-Gomez +5
Self-supervised pretrained models exhibit competitive performance in automatic speech recognition on finetuning, even with limited in-domain supervised data. However, popular pretr…
Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri +1
We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interv…
Implementing contextual biasing in GPU decoder for online ASR
Iuliia Nigmatulina, Srikanth Madikeri, Esaú Villatoro-Tello +4
GPU decoding significantly accelerates the output of ASR predictions. While GPUs are already being used for online ASR decoding, post-processing and rescoring on GPUs have not been…
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding
Juan Zuluaga-Gomez, Iuliia Nigmatulina, Amrutha Prasad +8
Voice communication between air traffic controllers (ATCos) and pilots is critical for ensuring safe and efficient air traffic control (ATC). This task requires high levels of awar…
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
Esaú Villatoro-Tello, Srikanth Madikeri, Juan Zuluaga-Gomez +6
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup. We benc…
A Comparison of Methods for OOV-word Recognition on a New Public Dataset
Rudolf A. Braun, Srikanth Madikeri, Petr Motlicek
A common problem for automatic speech recognition systems is how to recognize words that they did not see during training. Currently there is no established method of evaluating di…
Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model
Apoorv Vyas, Srikanth Madikeri, Hervé Bourlard
In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduc…