5 citations · 8 across the 13 of their papers we have counts for
14 papers
SEAL: Speaker Error Correction using Acoustic-conditioned Large Language Models
Anurag Kumar, Rohit Paturi, Amber Afshan +1
Speaker Diarization (SD) is a crucial component of modern end-to-end ASR pipelines. Traditional SD systems, which are typically audio-based and operate independently of ASR, often…
CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation
Han He, Qianchu Liu, Lei Xu +4
Existing automatic prompt engineering methods are typically designed for discriminative tasks, where new task prompts are iteratively refined with limited feedback from a single me…
Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization
Xiang Li, Vivek Govindan, Rohit Paturi +1
End-to-end neural diarization (EEND) models offer significant improvements over traditional embedding-based Speaker Diarization (SD) approaches but falls short on generalizing to l…
AG-LSEC: Audio Grounded Lexical Speaker Error Correction
Rohit Paturi, Xiang Li, Sundararajan Srinivasan
Speaker Diarization (SD) systems are typically audio-based and operate independently of the ASR system in traditional speech transcription pipelines and can have speaker errors due…
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…
SpeechVerse: A Large-scale Generalizable Audio Language Model
Nilaksh Das, Saket Dingliwal, Srikanth Ronanki +14
Large language models (LLMs) have shown incredible proficiency in performing tasks that require semantic understanding of natural language instructions. Recently, many works have f…
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech Translation
Juan Zuluaga-Gomez, Zhaocheng Huang, Xing Niu +5
Conventional speech-to-text translation (ST) systems are trained on single-speaker utterances, and they may not generalize to real-life scenarios where the audio contains conversat…
Speaker Diarization of Scripted Audiovisual Content
Yogesh Virkar, Brian Thompson, Rohit Paturi +2
The media localization industry usually requires a verbatim script of the final film or TV production in order to create subtitles or dubbing scripts in a foreign language. In part…
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction
Rohit Paturi, Sundararajan Srinivasan, Xiang Li
Speaker diarization (SD) is typically used with an automatic speech recognition (ASR) system to ascribe speaker labels to recognized words. The conventional approach reconciles out…
Device Directedness with Contextual Cues for Spoken Dialog Systems
Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati +2
In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins. Fol…
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition
Sundararajan Srinivasan, Zhaocheng Huang, Katrin Kirchhoff
Generic pre-trained speech and text representations promise to reduce the need for large labeled datasets on specific speech and language tasks. However, it is not clear how to eff…
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech
Rohit Paturi, Sundararajan Srinivasan, Katrin Kirchhoff +1
Many of the recent advances in speech separation are primarily aimed at synthetic mixtures of short audio utterances with high degrees of overlap. Most of these approaches need an…
Speaker-conversation factorial designs for diarization error analysis
Scott Seyfarth, Sundararajan Srinivasan, Katrin Kirchhoff
Speaker diarization accuracy can be affected by both acoustics and conversation characteristics. Determining the cause of diarization errors is difficult because speaker voice acou…
Best of Both Worlds: Robust Accented Speech Recognition with Adversarial Transfer Learning
Nilaksh Das, Sravan Bodapati, Monica Sunkara +2
Training deep neural networks for automatic speech recognition (ASR) requires large amounts of transcribed speech. This becomes a bottleneck for training robust models for accented…