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Found 12 papers, 6 papers with code
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling.
Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents
We show that users spend a lot of ``effort'' on these questions in terms of signals like clicks and session length, and that they are also challenging for GPT-4.
Towards audio language modeling - an overview
Neural audio codecs are initially introduced to compress audio data into compact codes to reduce transmission latency.
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance.
GSQA: An End-to-End Model for Generative Spoken Question Answering
Furthermore, the GSQA model has only been fine-tuned on the spoken extractive QA dataset.
Towards General-Purpose Text-Instruction-Guided Voice Conversion
This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice".
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks.
T5lephone: Bridging Speech and Text Self-supervised Models for Spoken Language Understanding via Phoneme level T5
In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e. g. HuBERT) and pretrained language models (PLM, e. g. T5).
DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering
We empirically showed that DUAL yields results comparable to those obtained by cascading ASR and text QA model and robust to real-world data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Controllability of Educational Question Generation by Keyword Provision
While the significant advancement of QG techniques was reported, current QG results are not ideal for educational reading practice assessment in terms of \textit{controllability} and \textit{question difficulty}.
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies.
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
Ranked #1 on
Distractor Generation
on RACE
