Search Results for author:
Found 14 papers, 8 papers with code
Aligning Speech to Languages to Enhance Code-switching Speech Recognition
Performance evaluation using large language models reveals the advantage of the linguistic hint by achieving 14. 1% and 5. 5% relative improvement on test sets of the ASRU and SEAME datasets, respectively.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection
In addition, this approach is not only valuable for the detection of depression but also represents a new perspective in enhancing the ability of LLMs to comprehend and process speech signals.
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model
Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks.
A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors
There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Generative error correction for code-switching speech recognition using large language models
In this work, we propose to leverage large language models (LLMs) and lists of hypotheses generated by an ASR to address the CS problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Enhancing Code-switching Speech Recognition with Interactive Language Biases
Languages usually switch within a multilingual speech signal, especially in a bilingual society.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex
While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored.
MERLIon CCS Challenge Evaluation Plan
This paper introduces the inaugural Multilingual Everyday Recordings- Language Identification on Code-Switched Child-Directed Speech (MERLIon CCS) Challenge, focused on developing robust language identification and language diarization systems that are reliable for non-standard, accented, spontaneous code-switched, child-directed speech collected via Zoom.
Investigating model performance in language identification: beyond simple error statistics
These overview metrics do not provide information about model performance at the level of individual speakers, recordings, or units of speech with different linguistic characteristics.
MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization
To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns.
Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization
The comparison of the proposed methods indicates that incorporating language information is more effective than disentangling for reducing language confusion in CS speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection
In this work, we propose a highly Portable Quantum Language Model (PQLM) that can easily transmit information to downstream tasks on classical machines.
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification
We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training.
Enhance Language Identification using Dual-mode Model with Knowledge Distillation
In this paper, we propose to employ a dual-mode framework on the x-vector self-attention (XSA-LID) model with knowledge distillation (KD) to enhance its language identification (LID) performance for both long and short utterances.
