1 citations · 2 across the 14 of their papers we have counts for
14 papers
Improving Code-Switching ASR with Code-Mixing Guided Synthetic Speech
Yue Heng Yeo, Haoyang Li, Yizhou Peng +6
Code-switch (CS) Automatic Speech Recognition (ASR) remains challenging due to limited availability of high quality CS text-speech pairs for training. Although synthetic data augme…
Unlocking Cognitive Capabilities and Analyzing the Perception-Logic Trade-off
Longyin Zhang, Shuo Sun, Yingxu He +15
Recent advancements in Multimodal Large Language Models (MLLMs) pursue omni-perception capabilities, yet integrating robust sensory grounding with complex reasoning remains a chall…
MERaLiON-SER: Robust Speech Emotion Recognition Model for English and SEA Languages
Hardik B. Sailor, Aw Ai Ti, Chen Fang Yih Nancy +26
We present MERaLiON-SER, a robust speech emotion recognition model designed for English and Southeast Asian languages. The model is trained using a hybrid objective combining weigh…
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data
Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu +7
Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech cruc…
Interpolating Speaker Identities in Embedding Space for Data Expansion
Tianchi Liu, Ruijie Tao, Qiongqiong Wang +5
The success of deep learning-based speaker verification systems is largely attributed to access to large-scale and diverse speaker identity data. However, collecting data from more…
Incorporating Contextual Paralinguistic Understanding in Large Speech-Language Models
Qiongqiong Wang, Hardik B. Sailor, Jeremy H. M. Wong +6
Current large speech language models (Speech-LLMs) often exhibit limitations in empathetic reasoning, primarily due to the absence of training datasets that integrate both contextu…
Contextual Paralinguistic Data Creation for Multi-Modal Speech-LLM: Data Condensation and Spoken QA Generation
Qiongqiong Wang, Hardik B. Sailor, Tianchi Liu +1
Current speech-LLMs exhibit limited capability in contextual reasoning alongside paralinguistic understanding, primarily due to the lack of Question-Answer (QA) datasets that cover…
Beyond Classification: Towards Speech Emotion Reasoning with Multitask AudioLLMs
Wenyu Zhang, Yingxu He, Geyu Lin +9
Audio Large Language Models (AudioLLMs) have achieved strong results in semantic tasks like speech recognition and translation, but remain limited in modeling paralinguistic cues s…
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond
Muhammad Huzaifah, Geyu Lin, Tianchi Liu +8
This technical report describes the MERaLiON-SpeechEncoder, a foundation model designed to support a wide range of downstream speech applications. Developed as part of Singapore's…
Towards Quantifying and Reducing Language Mismatch Effects in Cross-Lingual Speech Anti-Spoofing
Tianchi Liu, Ivan Kukanov, Zihan Pan +3
The effects of language mismatch impact speech anti-spoofing systems, while investigations and quantification of these effects remain limited. Existing anti-spoofing datasets are m…
Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024
Anmol Guragain, Tianchi Liu, Zihan Pan +2
This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (Ct…
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection
Zihan Pan, Tianchi Liu, Hardik B. Sailor +1
Self-supervised learning (SSL) speech representation models, trained on large speech corpora, have demonstrated effectiveness in extracting hierarchical speech embeddings through m…
Adversarial synthesis based data-augmentation for code-switched spoken language identification
Parth Shastri, Chirag Patil, Poorval Wanere +3
Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays…
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech
Vishwanath Pratap Singh, Hardik Sailor, Supratik Bhattacharya +1
Training a robust Automatic Speech Recognition (ASR) system for children's speech recognition is a challenging task due to inherent differences in acoustic attributes of adult and…