Search Results for author:
Found 15 papers, 4 papers with code
AugSumm: towards generalizable speech summarization using synthetic labels from large language model
We tackle this challenge by proposing AugSumm, a method to leverage large language models (LLMs) as a proxy for human annotators to generate augmented summaries for training and evaluation.
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions
Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-specific models.
Ranked #1 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query.
Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech
In this paper, we propose an evaluation technique involving the training of an ASR model on synthetic speech and assessing its performance on real speech.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling.
Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data
Pre-training speech models on large volumes of data has achieved remarkable success.
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
To achieve comprehensive coverage of diverse speech tasks and harness instruction tuning, we invite the community to collaborate and contribute, facilitating the dynamic growth of the benchmark.
Augmenting text for spoken language understanding with Large Language Models
Using the generated text with JAT and TTS for spoken semantic parsing improves EM on STOP by 1. 4% and 2. 6% absolute for existing and new domains respectively.
BASS: Block-wise Adaptation for Speech Summarization
End-to-end speech summarization has been shown to improve performance over cascade baselines.
SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks
In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape.
Egocentric Audio-Visual Noise Suppression
In this paper, we first demonstrate that egocentric visual information is helpful for noise suppression.
XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers
Transformers are among the state of the art for many tasks in speech, vision, and natural language processing, among others.
Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition
Accordingly, models that have been proposed for emotion detection use one or the other of these label types.
Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction
This work presents a multitask approach to the simultaneous estimation of age, country of origin, and emotion given vocal burst audio for the 2022 ICML Expressive Vocalizations Challenge ExVo-MultiTask track.
Speech Summarization using Restricted Self-Attention
End-to-end modeling of speech summarization models is challenging due to memory and compute constraints arising from long input audio sequences.
