Papers by Ariya Rastrow
Aligning Paralinguistic Understanding and Generation in Speech LLMs via Multi-Task Reinforcement Learning (2026.eacl-industry)
Copied to clipboard
Minseok Kim, Jingxiang Chen, Seong-Gyun Leem, Yin Huang, Rashi Rungta, Zhicheng Ouyang, Haibin Wu, Surya Teja Appini, Ankur Bansal, Yang Bai, Yue Liu, Florian Metze, Ahmed A Aly, Anuj Kumar, Ariya Rastrow, Zhaojiang Lin
| Challenge: | Using paralinguistic cues is challenging for speech large language models, authors say . limited training data, annotation difficulty, and models exploiting lexical shortcuts are challenges . a recent study shows that modeling paralinguistic reasoning with multitask RL improves paralinguistics understanding . |
| Approach: | They propose multi-task reinforcement learning with chain-of-thought prompting that elicits explicit affective reasoning. |
| Outcome: | The proposed model improves paralinguistics understanding over baselines and strong proprietary models by 8-12% on Expresso, IEMOCAP, and RAVDESS. |
Multi-Modal Retrieval For Large Language Model Based Speech Recognition (2024.findings-acl)
Copied to clipboard
Aditya Gourav, Jari Kolehmainen, Prashanth Shivakumar, Yile Gu, Grant Strimel, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko
| Challenge: | kNN-LM and cross-attention techniques are used to extend text based retrieval to other modalities . wide adoption of large language models has driven new application areas leveraging this technology . |
| Approach: | They propose to use kNN-LM and cross-attention techniques to extend text retrieval methods to other modalities. |
| Outcome: | The proposed methods outperform text-based retrieval and improve word error rate on a speech recognition dataset. |
Attention-based Contextual Language Model Adaptation for Speech Recognition (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing language models do not incorporate utterance level contextual information . however, for some domains like voice assistants, additional context provides a rich input signal . |
| Approach: | They propose a method for training neural speech recognition models on text and contextual data. |
| Outcome: | The proposed model reduces perplexity by 7.0% relative over a standard LM . it also improves perxicity by 2.8% relative to a state-of-the-art model for contextual LM. |